Two-dimensional Principal Component Analysis Research Articles

Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning.

In brain-computer interface (BCI) systems, symmetric positive definite (SPD) manifold within Riemannian space has been frequently utilized to extract spatial features from electroencephalogram (EEG) signals. However, the intrinsic high dimensionality of SPD matrices introduces too much computational burden to hinder the real-time applications of such BCI, especially in handling dynamic tasks, like incremental learning. Directly reducing the dimensionality of SPD matrices with conventional dimensionality reduction (DR) methods will alter the fundamental properties of SPD matrices. Moreover, current DR methods for incremental learning always necessitate retaining old data to update their representations under new mapping. To this end, a bidirectional two-dimensional principal component analysis for SPD manifold (B2DPCA-SPD) is proposed to reduce the dimensionality of SPD matrices, in such way that the reduced matrices remain on SPD manifold. Afterwards, the B2DPCA-SPD is extended to adapt to incremental learning tasks without saving old data. The incremental B2DPCA-SPD can be seamlessly integrated with the matrix-formed growing neural gas network (MF-GNG) to achieve an incremental EEG classification, where the new low-dimensional representations of the prototypes in old classifiers can be easily recalculated with the updated projection matrix. Extensive experiments are conducted on two public datasets to perform the EEG classification. The results demonstrate that our method significantly reduces computation time by 38.53% and 35.96%, and outperforms conventional methods in classification accuracy by 4.21% to 19.59%.

Face Image Recognition by using some Dimension Reduction Algorithms and Logistic Regression

Face recognition is a common technique for artificial intelligence and image processing in which machine-learning algorithms automatically recognize a person's face through a huge image collection or even from a live video since there is difficulty in detecting data when using different shots for the same person in a facial recognition system, it takes time to determine person's membership. Furthermore, when working with high-dimensional data, where the 2D-pixel values of facial images, can be computationally challenging, the face recognition problem gets harder, so the current research compares the dimensionality reduction techniques like the principal component analysis algorithm PCA, an unsupervised learning algorithm that reduces dimensions by moving them from a high-dimensional area to a lower area without losing important information, and the two-dimensional principal component analysis algorithm 2DPCA to determine which technique is most effective for the process of detecting facial images, and this method will be compared with the logistic regression which is a statistical model for face recognition that has been used for classification tasks. Two different sets of data were used, the first group represented the ORL database, which was made up of 40 individuals, while the second group represented the real data, which was made up of 100 individuals from AL-Mamoon College, the results showed that the logistic regression model achieved the lowest MSE for ORL dataset and real data by achieving (0.0024414, 0.091936) respectively, followed by 2DPCA and PCA and all three techniques reached the highest accuracy rate of 100% for facial image recognition on real data. Paper type: Research paper

Fault diagnoses of a nonlinear cracked rotor-bearing system based on vibration energy space and incremental learning approach

Health services of rotor-bearing systems are directly related to safe and stable works of rotating machineries in the industrial production. To address the issues of a low impact on vibration characteristics caused by faults in the higher rotational speed region, narrow and weak excitation signals, high dimensionality related to fault induction mechanisms, and difficulty in diagnosing using artificial intelligence technology due to the small number of fault samples in rotor systems, a novel fault detection method of nonlinear rotor systems through the incremental two-dimensional principal component analysis (I2DPCA) of the machine learning on the vibration energy space is proposed. Unlike mainly analyzing dynamic characteristics of rotor-bearing systems in vibration space, the method applies signals of the vibration energy space such as energy tracks, energy-time histories and energy-frequency spectra to have more advantages in vibration amplitudes caused by cracks in the higher rotational speed region and to overcome narrow and weak signals in the early fault stage. Then, the I2DPCA is applied to extract online low dimensional fault features and to process small vibration features of the rotor system. Simulation and experiment results show that performances of between-class margins and within-class clusters of different health conditions on the vibration energy space are more perfect than that on the vibration space. Based on the I2DPCA algorithm and the energy space method, the recognition rate of crack faults within the high rotational speed region is up to 99.6%, and it has good convergence and online learning ability in the case of small samples. The achieved conclusions of the method could provide a novel reference direction for fault diagnoses of rotor systems.

Quaternion-based 2D-DOST and stacked principal component analysis network for multimodal face recognition

Face recognition is widely utilized and has become a ubiquitous part of daily lives. However, original face images are often sensitive and unauthorized access could imperil personal information. To protect privacy and security of multimodal face data, we propose an original approach that integrates differential privacy and a lightweight convolutional neural network with unsupervised predefined quaternion-type filters. Firstly, different modalities of face images are banded together by means of full quaternion matrix representation for simultaneously processing. Then the proposed quaternion two-dimensional discrete orthogonal Stockwell transform is exploited for implementing difference privacy, which is disturbed by virtue of Laplace and exponential mechanism. Afterwards, we devise a stacked quaternion two-dimensional principal component analysis network with three-stages for learning representative features. The proposed network model can enhance the discriminative characteristics through inserting nonlinearity, together with union of feature maps in different levels to achieve information complementation. Experiments conducted on four multimodal face datasets unveil that the proposed method achieves superior recognition performance in comparison with other state-of-the-art approaches.

Comparative metabolomics analysis of metabolic pathways in the high-yielding mutant strain of avilamycin

Avilamycin (AVI) is an oligosaccharide antibiotic that has strong inhibitory effect on Gram-positive bacteria. It is widely used in livestock and poultry farming. However, the use of traditional breeding techniques and immature fermentation process have become the key factors limiting its commercialization. In this study, we used comparative metabolomics techniques to examine the difference in intracellular metabolism between a high-yield AVI mutant strain modified by ribosome engineering technology and the parental strain. GC-MS analysis was conducted on mycelia samples taken on days 4, 6, and 8 of fermentation, resulting in the detection of a total of 112 compounds. After comparison with the NIST library, 29 intracellular metabolites were accurately identified. Two-dimensional principal component analysis (PCA) revealed significant differences in metabolites between the mutant strain and the parental strain at different time points. Orthogonal partial least squares-discriminant analysis (OPLS-DA) identified 11 intracellular metabolites that were closely related to AVI biosynthesis. KEGG metabolic pathway enrichment analysis showed that avilamycin synthesis was closely related to carbohydrate metabolism and amino acid metabolism. Six key differential metabolites were selected: L-valine, L-serine, L-alanine, D-galactose, D-cellobiose, and D-glucose. Upregulation of these metabolites in the mutant strain enhanced its metabolic flow for AVI synthesis. After 8 days of fermentation, the mutant strain produced 76.86% more AVI than the parental strain. The findings of this study serve as reference for the future rational optimization of avilamycin fermentation.

Human and ecological risk assessments of potentially toxic elements in sediments around a pharmaceutical industry

Potentially toxic elements (PTEs) in sediment can be highly hazardous to the environment and public health. This study aimed to assess the human and ecological risks of PTEs in sediments around a pharmaceutical industry in Ilorin, Nigeria. Physicochemical parameters and the concentrations of lead (Pb), chromium (Cr), cadmium (Cd), cobalt (Co), arsenic (As), and nickel (Ni) were analyzed in sediment samples collected from seven locations in the wet and dry seasons. Standard two-dimensional principal component analysis (PCA) and risk assessments were also conducted. The concentrations of Pb, Co, Ni, Cr, Cd, and As in the sediments ranged from 0.001 to 0.031 mg/kg, 0–0.005 mg/kg, 0.005–0.012 mg/kg, 0.001–0.014 mg/kg, 0.005–0.024 mg/kg, and 0.001–0.012 mg/kg, respectively. The mean concentrations of the total PTEs content were found in decreasing order of concentration: Pb > Cd > Ni > Cr > As > Co. PCA showed that some of the PTEs were highly concentrated in samples obtained at other locations as well as at the discharge point. The Hazard Index was mostly <1 across locations, indicating little to no probable non-cancerous effect. However, the incremental lifetime cancer risk for arsenic and nickel was high and required attention. The ecological risk assessment showed that lead and arsenic were the major PTEs pollutants in all locations. The study identifies PTEs profiles in sediments and emphasises the necessity of continual monitoring and action to stop long-term negative impacts on the local environment and public health.

Microwave Heartprint: A novel non-contact human identification technology based on cardiac micro-motion detection using ultra wideband bio-radar

The existing one-time identity authentication technology cannot continuously guarantee the legitimacy of user identity during the whole human-computer interaction session, and often requires active cooperation of users, which seriously limits the availability. This study proposes a new non-contact identity recognition technology based on cardiac micro-motion detection using ultra wideband (UWB) bio-radar. After the multi-point micro-motion echoes in the range dimension of the human heart surface area were continuously detected by ultra wideband bio-radar, the two-dimensional principal component analysis (2D-PCA) was exploited to extract the compressed features of the two-dimensional image matrix, namely the distance channel-heart beat sampling point (DC-HBP) matrix, in each accurate segmented heart beat cycle for identity recognition. In the practical measurement experiment, based on the proposed multi-range-bin & 2D-PCA feature scheme along with two conventional reference feature schemes, three typical classifiers were selected as representatives to conduct the heart beat identification under two states of normal breathing and breath holding. The results showed that the multi-range-bin & 2D-PCA feature scheme proposed in this paper showed the best recognition effect. Compared with the optimal range-bin & overall heart beat feature scheme, our proposed scheme held an overall average recognition accuracy of 6.16% higher (normal respiration: 6.84%; breath holding: 5.48%). Compared with the multi-distance unit & whole heart beat feature scheme, the overall average accuracy increase was 27.42% (normal respiration: 28.63%; breath holding: 26.21%) for our proposed scheme. This study is expected to provide a new method of undisturbed, all-weather, non-contact and continuous identification for authentication.

Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis.

Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.

Sensory properties of selected biofortified common bean (Phaseolus vulgaris) varieties grown in Burundi.

The dry common bean is an important grain legume used for human consumption worldwide. In Eastern Africa, Burundi has a significantly high per capita consumption of the crop. There has been significant research on the underlying agronomic traits of dry biofortified common beans, such as disease resistance. However, there is limited systematic information describing the sensory properties of these bean varieties, particularly in Burundi. This study evaluated the sensory properties of eight cooked dry biofortified common bean varieties using a panel of fifty-four (fourteen plus forty) persons for descriptive sensory evaluation and consumer acceptability tests. Kinure, a traditional non-biofortified common bean variety, was the control. Based on differences in the attributes of the bean varieties, two-dimensional principal component analysis (PCA) explained 58.94% of the variation. The attributes of astringency, consistency, color, juiciness, beany aroma, stickiness, and bean size contributed mostly to the differentiation of the bean varieties. A 95% PCA prediction ellipse displayed stronger congruity in the descriptive attributes of NUV130, NUV91, RWV1129, RWV1272, and RWR2245. In contrast, a deviation in the descriptive attributes of MAC44, MAC70, and RWR2154 was discerned. Regarding consumer acceptability tests, the varieties RWR2245 and MAC44 garnered significantly higher (p < .05) sensory scores on color, aroma, taste, texture, and overall acceptability. Therefore, the physical traits of cooked biofortified common bean varieties are a major contributor to varietal disparities in consumer acceptance studies. These parameters can greatly impact the adoption of dry biofortified common beans and could be of concern to common bean breeders.

Investigating primary decomposition of polypropylene through detailed compositional analysis using two-dimensional gas chromatography and principal component analysis

This work systematically assessed the effects of pyrolysis parameters, such as sample weight, particle size, carrier gas flow rate, and temperature, on product speciation and distribution in the pyrolysis of polypropylene (PP) for intrinsic kinetics. This study employed a Box-Behnken design (BBD), along with the analysis of BBD boundary conditions, as well as conditions beyond the BBD to validate the pyrolysis conditions in the primary decomposition of PP using a micropyrolyzer coupled to two-dimensional chromatography with flame ionization and time-of-flight spectrometer detectors (Py–GC×GC–FID/TOF–MS). Statistical differences and similarities in the product distribution of the experiments were determined using principal component analysis (PCA). Characteristic time analysis indicated that the sample weights employed in the study were within the isothermal kinetically controlled region, with film thickness ranging from 4.85 to 14.46 µm. The analysis of 600 pyrolysis products identified and quantified in the pyrolysis of PP for the condition combinations evaluated in BBD resulted in statistical differences at temperatures of 460, 530, and 600 oC, with C3 to C35 yields ranging from 39.88 ± 2.08 to 66.61 ± 3.26 wt%. However, no statistical differences were found in the pyrolysis product distribution within the ranges of sample weight (50 – 150 µg), particle size (53 – 125, 125 – 300, and >300 µm), and carrier gas flow rate (100 – 300 ml min-1) studied in the BBD experimental space. The analysis of the border conditions showed that a combination of the lowest carrier gas flow rate (100 ml min-1) and the highest sample weight (150 μg) resulted in statistically different product distribution at the studied pyrolysis temperatures compared to the combination suggested by BBD. Conditions beyond the BBD, including a sample weight of 800 µg and a carrier gas flow rate of 30 ml min-1, resulted in statistically different product distributions compared to the BBD. Notably, new products such as polycyclic aromatic hydrocarbons (PAHs) were identified. The findings from this work clearly show the importance of selecting appropriate pyrolysis parameters to obtain kinetically relevant experimental data, which in turn serves as a valuable guide for the development and validation of (micro-) kinetic models.

Person Identification System Using Periocular Biometrics Based on Hybrid Optimal Dense Capsule Network

Person identification using periocular images has emerged as a challenging scenario in efficient biometric analysis, particularly under less constrained environments. Accurate recognition is significant in rendering effective measures during the COVID-19 pandemic. In this research paper, the person identification process is performed based on a deep learning model. Several effectual methods have already been developed, but certain drawbacks still exist, like deteriorated image quality, high computational cost, increased error, less training ability, a requirement of high storage space and accuracy rate degradation. Hence, the proposed work introduces a Hybrid Optimal Dense Capsule network-based Periocular biometric system (HodCP) to conquer these demerits. The proposed work involves pre-processing, dimensionality reduction, hybrid feature extraction and Image matching. The pre-processing step is undertaken using Parabolic Contrast Enhancement (PCE) to balance the image contrast and enhance the image quality. Then the Two-Dimensional Principal Component Analysis (2D_PCA) is employed to minimize the image dimensionality. Deep features are extracted in the hybrid feature extraction process using Dense Convolutional-121 Capsule Network (DenseCapsNet). The net loss and hyperparameter tuning are performed through African Vultures Optimization (AVO) algorithm. Finally, image matching is performed using Weighted Distance Similarity (WDS), which identifies the similarity between the query image and a set of image samples based on the distance score. The simulation tool used for analyzing better performance is PYTHON. The data required to process the proposed work are collected from four benchmark datasets. The proposed work provides a better accuracy rate of CASIA-Iris-Mobile-V1.0 (99.01%), UBIPr (99.12%), Facemask detection dataset (98.67%) and Glasses versus without glasses dataset (98.83%), which is superior to the existing methods.

Cosine 2DPCA With Weighted Projection Maximization.

Two-dimensional principal component analysis (2DPCA), known to be very sensitive to outliers, employs the square F -norm as the distance metric and only satisfies the optimal objective of maximizing projection variance. However, the objective of minimizing reconstruction errors for all samples is not optimized as much as possible. To handle the problem, a novel cosine objective function is first presented for maximizing weighted projection, in which the 2-norm of vectors with an adjustable power parameter is employed as the distance metric. Not only the objective with the maximum projection distance is accomplished in the cosine objective function, but also the objective with the minimum sum of reconstruction errors is also optimized indirectly. Then, the cosine 2DPCA (Cos-2DPCA) method is proposed, and the greedy iterative algorithm to solve Cos-2DPCA is also developed. The convergence and correlation of solutions are proved theoretically and discussed in detail. Finally, the series of experiments are carried out on the artificial dataset and eight standard datasets, respectively. The results demonstrate that the performances of Cos-2DPCA are significantly improved on the reconstruction, correlation, complexity, and classification, and it outperforms most of the existing robust 2DPCA methods.

Genetic divergence in common bean genotypes from the IRAD gene bank: morpho-agronomic characteristics, fungal and bacterial disease resistance, and opportunities for genetic improvement

For successful plant breeding in any crop species, the importance of diversity in the available germplasm population is known and established. Thirty-two common bean (Phaseolus vulgaris) genotypes from the IRAD gene bank in Cameroon were evaluated for divergence in terms of their morpho-agronomic traits, fungal disease resistance, and bacterial disease resistance to assess the opportunity for genetic improvement of the crop. The trait associations were estimated using correlation coefficients and genotypes were classified into groups using cluster and principal component analyses. Seven qualitative and 16 quantitative traits comprising growth, phenological, yield, and disease variables were evaluated in this study. The qualitative markers revealed the degree of polymorphism among the 32 common bean genotypes. The number of phenotypic classes per character (Na) ranged from 2 to 18, with an average of 5.14. The expected gene diversity (He) ranged from 0.37 to 0.93 (mean = 0.56). The number of effective phenotypic classes (Ne) ranged from 1.82 to 14.22, with a mean of 3.85. An extensive range of variation was evident for the majority of traits, highlighting their utility for characterizing common bean germplasm. Many qualitative traits, including seed coat color, seed shape, and seed size, and also some quantitative traits of economic importance including seed yield, were found to be highly variable within the collection, with the MAC55 genotype displaying the highest yield (32.65 g per plant). Four genotypes, namely MAC55, BOA-5-1M6, FEB 192, and Banguem showed resistance to the two main common bean diseases, angular leaf spot and common blight. We detected highly significant correlations among several traits related to yield. A high broad-sense heritability was found for most of the quantitative traits. We carried out two-dimensional principal component analysis and used hierarchical clustering to group the analyzed germplasm according to their phenotypic similitudes. The evidence of agro-morphological diversity in the present collection and the identification of discriminant characters between the available germplasm through the use of PCA analysis have significant implications for establishing breeding schemes in common bean.

Adaptive sampling and monitoring of partially observed images

Image-based monitoring techniques have achieved great success in many engineering applications. However, most existing image monitoring methods require fully observed images to implement modeling/monitoring. This requirement limits the application of these techniques for images with large size and/or cameras with limited scanning area. In this article, we propose to solve this issue by splitting the large image into multiple sub-images and using partially observed sub-images to implement monitoring of the whole large image. More specifically, the two-dimensional multivariate functional principal component analysis (2D-MFPCA) is proposed to model the cross-and-within correlation among sub-images, which facilitates the information augmentation from partially observed sub-images. The asymptotic properties of the 2D-MFPCA estimators are investigated to justify the use of MFPC scores as the monitoring statistics, which are fed into a multivariate CUSUM control chart to implement adaptive sampling and monitoring of image data. The developed chart can dynamically select locations of partially observed sub-images and adaptively focus on the most suspicious sub-images. The proposed method is validated and compared with various benchmark methods in both numerical and case studies. The results demonstrate the proposed method can achieve superior monitoring performance for large images when only partially observed sub-images are available.

Rapid detection and quantification of adulteration in saffron by excitation-emission matrix fluorescence combined with multi-way chemometrics.

Saffron has gained people's attention and love for its unique flavor and valuable edible value, but the problem of saffron adulteration in the market is serious. It is urgent for us to find a simple and rapid identification and quantitative estimation of adulteration in saffron. Therefore, excitation-emission matrix (EEM) fluorescence combined with multi-way chemometrics was proposed for the detection and quantification of adulteration in saffron. The fluorescence composition analysis of saffron and saffron adulterants (safflower, marigold and madder) were accomplished by alternating trilinear decomposition (ATLD) algorithm. ATLD and two-dimensional principal component analysis combined with k-nearest neighbor (ATLD-kNN and 2DPCA-kNN) and ATLD combined with data-driven soft independent modeling of class analogies (ATLD-DD-SIMCA) were applied to rapid detection of adulteration in saffron. 2DPCA-kNN and ATLD-DD-SIMCA methods were adopted for the classification of chemical EEM data, first with 100% correct classification rate. The content of adulteration of adulterated saffron was predicted by the N-way partial least squares regression (N-PLS) algorithm. In addition, new samples were correctly classified and the adulteration level in adulterated saffron was estimated semi-quantitatively, which verifies the reliability of these models. ATLD-DD-SIMCA and 2DPCA-kNN are recommended methods for the classification of pure saffron and adulterated saffron. The N-PLS algorithm shows potential in prediction of adulteration levels. These methods are expected to solve more complex problems in food authenticity. © 2023 Society of Chemical Industry.

Machine-Learning Aided NO2 Discrimination with an Array of Graphene Chemiresistors Covalently Functionalized by Diazonium Chemistry.

Boosted by the emerging need for highly integrated gas sensors in IoT ecosystems, electronic noses (e-noses) are gaining interest for the detection of specific molecules over a background of interfering gases. The sensing of nitrogen dioxide (NO2) is particularly relevant for applications in environmental monitoring and precision medicine. Here we present an easy and efficient functionalization procedure to covalently modify graphene layers, taking advantage of diazonium chemistry. Separate graphene layers were functionalised with one of three different aryl rings 4-nitrophenyl, 4-carboxyphenyl, and 4-bromophenyl). The distinct mod(ified graphene layers were assembled with a pristine layer into an e-nose for NO2 discrimination. A remarkable sensitivity to NO2 is demonstrated through exposures to gaseous solutions with NO2 concentrations in the 1-10 ppm range at room temperature. Then, the discrimination capability of the sensor array is tested by carrying out exposures to several interfering gases and analyzing the data through multivariate statistical analysis. This analysis shows that the e-nose can discriminate NO2 among all the interfering gases in a two-dimensional principal component analysis space. Finally, the e-nose is trained to accurately recognize NO2 contributions with a linear discriminant analysis approach, thus providing a metric for discrimination assessment with a prediction accuracy above 95%.

Exploring genetic diversity of Dahlia (Dahlia variabilis Desf.) germplasm using multivariate statistics

Dahlia (Dahlia variabilis) is a tuberous-rooted flower crop, exhibiting rich diversity in flower color and inflorescence form. The study was conducted to quantify diversity in 24 dahlia genotypes based on agronomic traits. The dahlia accessions were grouped based on their similarity for phenotypic resemblance following hierarchal clustering and principal component analysis (PCA). The hierarchical cluster analysis grouped the dahlia accessions into three distinct clusters viz., C1, C2 and C3 comprising 8, 3 and 13 genotypes, respectively. The 24 dahlia genotypes were found scattered across the whole variation observed by PC1 and PC2 (explaining nearly 55.2% of the cumulative total variation). The two-dimensional PCA analysis revealed that the most appropriate traits for grouping the dahlia accessions were plant height, flower weight, stalk length, vase life and number of flowers per plant. The study signifies the importance of germplasm collection, characterization and utilization of dahlia to popularize its commercial cultivation among the flower growers.

Qualitative and Difference Analysis of Sweet Cherry Fruit Volatile Organic Compounds under Different Cultivation Patterns Based on Gas Chromatography Ion Mobility Spectrometry

Gas chromatography ion mobility spectrometry (GC-IMS) was used to detect the volatile organic compounds (VOCs) of Zheng5–5 sweet cherry fruits cultivar under three cultivation patterns [arched cover (PN), umbrella cover (SX), and open field (LD)]. VOCs were analyzed and compared using three-dimensional and two-dimensional top view visualization, fingerprint analysis, and principal component analysis (PCA). A total of 24 VOCs (including monomers and dimers) were detected in PN, SX, and LD, and 19 of them were finally identified, mainly alcohols, aldehydes, acids, esters, and ketones. The VOCs of PN, SX, and LD were similar but significantly different in content. The three compounds with the highest relative content were 2-hexen-1-olD, ethanol, and hexanalD, with ranges of 13.71% (LD) to 22.31% (PN), 13.02% (SX) to 21.27% (LD), and 8.22% (LD) to 16.66% (PN), respectively. Esters are the main components that form the sweet cherry fruit aroma, with a relative content of LD&gt;PN&gt;SX. According to the PCA, fruit samples of PN, SX, and LD can be clearly distinguished, indicating significant differences in VOCs under different cultivation modes. The GC-IMS visual plots of PN, SX, and LD agreed with the PCA results, and their combination was suitable for characterizing the VOCs of sweet cherry fruits under different cultivation patterns. This study can provide a reference for evaluating the flavor characteristics of sweet cherry fruits with different cultivation patterns.

A joint-norm distance metric 2DPCA for robust dimensionality reduction

Two-dimensional principal component analysis (2DPCA) is one of the most representative dimensionality reduction methods and the robustness degrades in the presence of noise. To improve the robustness, several 2DPCA methods using similar distance metrics were proposed to achieve the objective of the maximum projection distance or the minimum reconstruction error. However, these two objectives cannot be accomplished simultaneously or are constrained by the same projection matrix. To handle these problems, we propose a generalized robust 2DPCA method called 2DPCA-2-Lp, which joins 2-norm and lp-norm metrics in the objective function. It aims to maximize the ratio of projected vectors to image row vectors and to fulfill the dual objectives of directly maximizing projection distances and indirectly minimizing reconstruction errors. 2DPCA-2-Lp measures the distances between row vectors of matrices rather than the distances between matrices, thus remarkably enhancing the robustness. Furthermore, the relationship between reconstruction errors and projection distances for different parameter values is analyzed theoretically under the joint-norm metric. Then, the closed greedy iterative algorithm is developed for obtaining optimal solutions. Finally, extensive experimental results on four datasets show that 2DPCA-2-Lp outperforms most existing 2DPCA methods in terms of reconstruction and classification performances and has better robustness against noise.

Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Image copy detection is an important technology of copyright protection. This paper proposes an efficient hashing method for image copy detection using 2D-2D (two-directional two-dimensional) PCA (Principal Component Analysis). The key is the discovery of the translation invariance of 2D-2D PCA. With the property of translation invariance, a novel model of extracting rotation-invariant low-dimensional features is designed by combining PCT (Polar Coordinate Transformation) and 2D-2D PCA. The PCT can convert an input rotated image to a translation matrix. Since the 2D-2D PCA is invariant to translation, the low-dimensional features learned from the translation matrix are rotation-invariant. Moreover, vector distances of low-dimensional features are stable to common digital operations and thus hash construction with the vector distances is of robustness and compactness. Three open image datasets are exploited to conduct various experiments for validating efficiencies of the proposed method. The results demonstrate that the proposed method is much better than some representative hashing methods in the performances of classification and copy detection.