Adaptive Sparse Representation Research Articles

Component adaptive sparse representation for hyperspectral image classification

Component adaptive sparse representation for hyperspectral image classification

Spectrum Reconstruction of Multispectral Light Field Imager Based on Adaptive Sparse Representation

Spectrum Reconstruction of Multispectral Light Field Imager Based on Adaptive Sparse Representation

Self-organizing Competitive Neural Network Based Adaptive Sparse Representation for Magnetotelluric Data Denoising

The existing sparse decomposition denoising methods for magnetotelluric (MT) data need to set the iterative stop condition manually, which not only has a large workload and high difficulty, but also easily causes subjective bias. To this end, we propose a new adaptive sparse representation method for MT data denoising. First, the data to be processed is divided into high-quality segments and noisy segments by machine learning algorithm. Then, the characteristic parameters of high-quality segments are calculated, and the boundary value of the characteristic parameters is taken as the threshold. The threshold has two functions, one is as a criterion for signal-to-noise identification, and the other is as an iterative stop condition for subsequent sparse decomposition. Finally, the optimized orthogonal matching pursuit algorithm is used to separate the signal and noise of the noisy segments, and the denoised segments and high-quality segments are combined to obtain the complete denoised MT data. The field data processing results show that this method is a fully automatic and intelligent MT data denoising method. It greatly improves the signal-to-noise ratio and the apparent resistivity-phase curves.

Face Recognition Method under Adaptive Image Matching and Dictionary Learning Algorithm.

In this research, a robust face recognition method based on adaptive image matching and a dictionary learning algorithm was proposed. A Fisher discriminant constraint was introduced into the dictionary learning algorithm program so that the dictionary had certain category discrimination ability. The purpose was to use this technology to reduce the influence of pollution, absence, and other factors on face recognition and improve the recognition rate. The optimization method was used to solve the loop iteration to obtain the expected specific dictionary, and the selected specific dictionary was used as the representation dictionary in adaptive sparse representation. In addition, if a specific dictionary was placed in a seed space of the original training data, the mapping matrix can be used to represent the mapping relationship between the specific dictionary and the original training sample, and the test sample could be corrected according to the mapping matrix to remove the contamination in the test sample. Moreover, the feature face method and dimension reduction method were used to process the specific dictionary and the corrected test sample, and the dimensions were reduced to 25, 50, 75, 100, 125, and 150, respectively. In this research, the recognition rate of the algorithm in 50 dimensions was lower than that of the discriminatory low-rank representation method (DLRR), and the recognition rate in other dimensions was the highest. The adaptive image matching classifier was used for classification and recognition. The experimental results showed that the proposed algorithm had a good recognition rate and good robustness against noise, pollution, and occlusion. Health condition prediction based on face recognition technology has the advantages of being noninvasive and convenient operation.

A Condition-Monitoring Approach for Diesel Engines Based on an Adaptive VMD and Sparse Representation Theory

This paper presents a novel method for condition monitoring using the RMS residual of vibration signal reconstruction based on trained dictionaries through sparse representation theory. Measured signals were firstly decomposed into intrinsic mode functions (IMFs) for training the initial dictionary. In this step, an adaptive variational mode decomposition (VMD) was proposed for providing information with higher accuracy, and the decompositions were used as discriminative atoms for sparse representation. Then, the overcomplete dictionary for sparse coding was learned from IMFs to reserve the highlight feature of the signals. As the dictionaries were trained, newly measured signals could be directly reconstructed without any signal decompositions or dictionary learning. This meant errors likely introduced by signal process techniques, such as VMD, EMD, etc., could be excluded from the condition monitoring. Moreover, the efficiency of the fault diagnosis was greatly improved, as the reconstruction was fast, which showed a great potential in online diagnosis. The RMS of the residuals between the reconstructed and measured signals was extracted as a feature of condition. A case study on operating condition identification of a diesel engine was carried out experimentally based on vibration accelerations, which validated the availability of the proposed feature extraction and condition-monitoring approach. The presented results showed that the proposed method resulted in a great improvement in the fault feature extraction and condition monitoring, and is a promising approach for future research.

Multiview Clustering of Adaptive Sparse Representation Based on Coupled P Systems

A multiview clustering (MVC) has been a significant technique to dispose data mining issues. Most of the existing studies on this topic adopt a fixed number of neighbors when constructing the similarity matrix of each view, like single-view clustering. However, this may reduce the clustering effect due to the diversity of multiview data sources. Moreover, most MVC utilizes iterative optimization to obtain clustering results, which consumes a significant amount of time. Therefore, this paper proposes a multiview clustering of adaptive sparse representation based on coupled P system (MVCS-CP) without iteration. The whole algorithm flow runs in the coupled P system. Firstly, the natural neighbor search algorithm without parameters automatically determines the number of neighbors of each view. In turn, manifold learning and sparse representation are employed to construct the similarity matrix, which preserves the internal geometry of the views. Next, a soft thresholding operator is introduced to form the unified graph to gain the clustering results. The experimental results on nine real datasets indicate that the MVCS-CP outperforms other state-of-the-art comparison algorithms.

A Novel Fault Diagnosis Approach for Rolling Bearing Based on CWT and Adaptive Sparse Representation

Extraction and enhancement of weak impulse signature is the key of rolling bearing fault prognostics in which case the features are often weak and covered by noise. Tunable Q-factor wavelet transform (TQWT), as an emerging wavelet construction theory developed in a frequency domain explicitly, has the advantages of matching with the specific oscillation behavior of signal components. In this article, an adaptive sparse representation (ASR) method is proposed, which integrates the sparse code shrinkage (SCS) and parameter optimization into TQWT. However, direct application of ASR is difficult to extract fault signatures at the early stage or low-speed operation due to weak fault symptoms and background noise. A novel fault diagnosis strategy based on continuous wavelet transform (CWT) and ASR is investigated. CWT owns significant advantages on multiscale subdivision and weak signal detection. The results of simulated and experimental vibration signal analyses verify the effectiveness of the proposed method in accurately extracting weak impulse features from the noise environment.

A method for magnetotelluric data processing based on sparsity adaptive stage-wise orthogonal matching pursuit

In order to effectively suppress large-scale strong interference and retain the useful low-frequency information of the measured magnetotelluric (MT) data, a new method of MT signal-noise separation based on sparsity adaptive stage-wise orthogonal matching pursuit (SAStOMP) is proposed. To demonstrate this technique, pulse atoms, cosine atoms and other atoms were used to build an over-complete dictionary, and fuzzy entropy was applied to control the sparsity of improved stage-wise orthogonal matching pursuit (IStOMP) algorithm, thereby realizing adaptive sparse representation. Then, we utilized SAStOMP to suppress strong electromagnetic interference and reconstruct useful MT signals. The technique was validated through simulated typical strong interference and measured MT data in ore district. The findings have proved that the proposed method can effectively eliminate large-scale strong interference and retain low-frequency useful signals in comparison with conventional methods such as remote reference (RR), orthogonal matching pursuit (OMP), and stage-wise orthogonal matching pursuit (StOMP). The obtained apparent resistivity-phase curve is smoother and more consistent, and the reflected underground electrical structure is more realistic and reliable.

Research on Magnetotelluric Long-Duration Noise Reduction Based on Adaptive Sparse Representation

Research on Magnetotelluric Long-Duration Noise Reduction Based on Adaptive Sparse Representation

Adaptive Sparse Representation-Based Minimum Entropy Deconvolution for Bearing Fault Detection

Deconvolution methods have been extensively applied for enhancing the impulsive feature of those bearing signals, which promotes the accuracy of the bearing fault diagnosis. However, these methods are sensitive to the invers filters length, which leads to poor effect. To overcome the existing problems, a new adaptive sparse representation-based minimum entropy deconvolution (AdaSRMED) is proposed on basis of the impulsive feature extraction of sparse representation, which promotes the robustness for inverse filter length and its effectiveness of impulse enhancement. In AdaSRMED, a new adaptive sparse representation (SR) is proposed based on the features of fault impulsive signals for solving these existing problems of previous SR methods. Moreover, the new adaptive SR is integrated with the minimum entropy deconvolution (MED) for improving the performance of MED, which avoids the shortcomings of MED and MED-related methods. A series of simulation signal and real fault signal experiments are performed to illustrate the superiority of AdaSRMED in fault detection. The comparison analysis with the conventional MED and improved MEDs shows that AdaSRMED can effectively enhance the impulsive features and show good bearing fault detection performance.

Image Compression Using Stochastic-AFD Based Multisignal Sparse Representation.

Adaptive Fourier decomposition (AFD) is a newly developed signal processing tool that can adaptively decompose any single signal using a Szegö kernel dictionary. To process multiple signals, a novel stochastic-AFD (SAFD) theory was recently proposed. The innovation of this study is twofold. First, a SAFD-based general multi-signal sparse representation learning algorithm is designed and implemented for the first time in the literature, which can be used in many signal and image processing areas. Second, a novel SAFD based image compression framework is proposed. The algorithm design and implementation of the SAFD theory and image compression methods are presented in detail. The proposed compression methods are compared with 13 other state-of-the-art compression methods, including JPEG, JPEG2000, BPG, and other popular deep learning-based methods. The experimental results show that our methods achieve the best balanced performance. The proposed methods are based on single image adaptive sparse representation learning, and they require no pre-training. In addition, the decompression quality or compression efficiency can be easily adjusted by a single parameter, that is, the decomposition level. Our method is supported by a solid mathematical foundation, which has the potential to become a new core technology in image compression.

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

Over the last two decades, radiologists have been using multi-view images to detect tumors. Computer Tomography (CT) imaging is considered as one of the reliable imaging techniques. Many medical-image-processing techniques have been developed to diagnoses lung cancer at early or later stages through CT images; however, it is still a big challenge to improve the accuracy and sensitivity of the algorithms. In this paper, we propose an algorithm based on image fusion for lung segmentation to optimize lung cancer diagnosis. The image fusion technique was developed through Laplacian Pyramid (LP) decomposition along with Adaptive Sparse Representation (ASR). The suggested fusion technique fragments medical images into different sizes using the LP. After that, the LP is used to fuse the four decomposed layers. For the evaluation purposes of the proposed technique, the Lungs Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) was used. The results showed that the Dice Similarity Coefficient (DSC) index of our proposed method was 0.9929, which is better than recently published results. Furthermore, the values of other evaluation parameters such as the sensitivity, specificity, and accuracy were 89%, 98% and 99%, respectively, which are also competitive with the recently published results.

Adaptive Sparse Representation of Continuous Input for Tsetlin Machines Based on Stochastic Searching on the Line

This paper introduces a novel approach to representing continuous inputs in Tsetlin Machines (TMs). Instead of using one Tsetlin Automaton (TA) for every unique threshold found when Booleanizing continuous input, we employ two Stochastic Searching on the Line (SSL) automata to learn discriminative lower and upper bounds. The two resulting Boolean features are adapted to the rest of the clause by equipping each clause with its own team of SSLs, which update the bounds during the learning process. Two standard TAs finally decide whether to include the resulting features as part of the clause. In this way, only four automata altogether represent one continuous feature (instead of potentially hundreds of them). We evaluate the performance of the new scheme empirically using five datasets, along with a study of interpretability. On average, TMs with SSL feature representation use 4.3 times fewer literals than the TM with static threshold-based features. Furthermore, in terms of average memory usage and F1-Score, our approach outperforms simple Multi-Layered Artificial Neural Networks, Decision Trees, Support Vector Machines, K-Nearest Neighbor, Random Forest, Gradient Boosted Trees (XGBoost), and Explainable Boosting Machines (EBMs), as well as the standard and real-value weighted TMs. Our approach further outperforms Neural Additive Models on Fraud Detection and StructureBoost on CA-58 in terms of the Area Under Curve while performing competitively on COMPAS.

Kernel eigenmaps based multiscale sparse model for hyperspectral image classification

Hyperspectral imaging (HSI) is the emerging method that combines traditional imaging and spectroscopy to provide the image with both the spatial and spectral information of the object present in the image. The major challenges of the existing techniques for HSI classification are the high dimensionality of data and its complexity in classification. This paper devises a new technique to classify the HSI named Spatial–Spectral Schroedinger Eigen Maps based Multi-scale adaptive sparse representation (S2SEMASR). In this, two different phases are employed for the accurate classification of the HSI, namely, Schroedinger Eigen maps (SE) based spatial–spectral feature extraction and multi-scale adaptive sparse classification for the feature extracted image. SE makes use of spatial–spectral cluster potentials which allows the extraction of features that best describes the characteristics of different classes of HSI. The multiscale adaptive sparse representation (MASR) applied over the SE features provides the sparse coefficients that includes distinct scale level sparsity with same class level sparsity. With the obtained coefficients, the class label of each pixel is determined. The proposed HSI classifier well utilizes the spectral and spatial characteristics to exploit the within-class variability and thus reduces the misclassification of similar test pixels Experimental results demonstrated that the proposed S2SEMASR approach outperforms the traditional results both qualitatively and quantitatively with an overall accuracy of 98.3%.

Weak fault signature identification of rolling bearings based on improved adaptive compressed sensing method

In order to extract the weak fault feature mixed in the background noise and escape the constraints of traditional sampling theorems on large-capacity data collection, transmission and storage, an improved compressed sensing (CS) algorithm, in which an adaptive sparse representation based on learning dictionary and an improved reconstruction scheme are integrated, is presented in this paper. The adaptive sparse representation method includes the K-singular value decomposition based dictionary learning algorithm, and the stagewise weak orthogonal matching pursuit reconstruction is improved by the regularization principle, backtracking mechanism, adaptive mechanism and relevant stop criteria. The compressive measurement was completed through Gaussian random matrix. One simulation signal and two different experimental data sets of rolling bearing are utilized to validate the effectiveness of the proposed method, and the results have shown that the reconstruction accuracy and calculation efficiency can be improved simultaneously. Most importantly, the weak fault feature shaded by strong background noise can be effectively identified, which cannot be obtained by traditional CS method.

EEG motor imagery classification using dynamic connectivity patterns and convolutional autoencoder

Recently, Brain computer Interface (BCI), plays an important role in recognizing brain activities and rehabilitation. Motor imagery (MI) classification based on Electroencephalography (EEG) signal analysis has received a lot of attention for the purpose of movement intent recognition. In this paper, we propose a novel feature extraction method which leads to promising MI classification performance. We split the individual EEG channels’ time series into temporal blocks and compute connectivity matrices for each block using adaptive sparse representation (ASR). The connectivity matrices indicate the correlation among different channels’ temporal blocks. They construct the dynamic connectivity patterns which are 3D tensors. Then, kernel PCA or non-linear convolutional autoencoders are applied to these tensors to learn discriminatory representations. This way, we incorporate the joint spatial-temporal structure of the EEG signals in the acquired features. We use a Gradient boosting model for classification. It is shown that the proposed feature extraction approach can lead to promising EEG MI classification performance compared to the state-of-the-art methods.

A sparse representation denoising algorithm for finger-vein image based on dictionary learning

As an important method of biometric authentication, finger-vein recognition utilizes the unique finger-vein patterns to identify individuals at a high level of accuracy and safety. However, noise components, inherent in finger-vein images, pose a formidable challenge for extracting reliable finger-vein features for recognition. To tackle this challenge, intensive efforts have been directed at sparse representation (SR) methods, which can find the best representative of a test sample by a sparse linear combination of training samples (atoms) from a dictionary. Previous SR approaches treat training atoms equally for image representations, even if these atoms may vary in their effectiveness as feature descriptors, thus jeopardizing the denoising and recognition performances. To overcome this limitation, the present study proposed an adaptive SR with distance-based dictionary learning (DDL), enabling the ability to target more informative training samples. Specifically, based on the Euclidean distance, atoms in the dictionary are classified into two groups: the high-information and the low-information. Their weights for feature representations are assigned based on the distance entropy. Experimental results indicate that the developed SR-DDL denoising method, can suppress image noises and subsequently enhance the image recognition performance.

Joint Hapke Model and Spatial Adaptive Sparse Representation with Iterative Background Purification for Martian Serpentine Detection

Visible and infrared imaging spectroscopy have greatly revolutionized our understanding of the diversity of minerals on Mars. Characterizing the mineral distribution on Mars is essential for understanding its geologic evolution and past habitability. The traditional handcrafted spectral index could be ambiguous as it may denote broad mineralogical classes, making this method unsuitable for definitive mineral investigation. In this work, the target detection technique is introduced for specific mineral mapping. We have developed a new subpixel mineral detection method by joining the Hapke model and spatially adaptive sparse representation method. Additionally, an iterative background dictionary purification strategy is proposed to obtain robust detection results. Laboratory hyperspectral image containing Mars Global Simulant and serpentine mixtures was used to evaluate and tailor the proposed method. Compared with the conventional target detection algorithms, including constrained energy minimization, matched filter, hierarchical constrained energy minimization, sparse representation for target detection, and spatially adaptive sparse representation method, the proposed algorithm has a significant improvement in accuracy about 30.14%, 29.67%, 29.41%, 9.13%, and 8.17%, respectively. Our algorithm can detect subpixel serpentine with an abundance as low as 2.5% in laboratory data. Then the proposed algorithm was applied to two well-studied Compact Reconnaissance Imaging Spectrometer for Mars images, which contain serpentine outcrops. Our results are not only consistent with the spatial distribution of Fe/Mg phyllosilicates derived by spectral indexes, but also denote what the specific mineral is. Experimental results show that the proposed algorithm enables the search for subpixel, low-abundance, and scientifically valuable mineral deposits.

Adaptive Nonnegative Sparse Representation for Hyperspectral Image Super-Resolution

As the hyperspectral images (HSIs) usually have a low spatial resolution, HSI super-resolution has recently attracted more and more attention to enhance the spatial resolution of HSIs. A common method is to fuse the low-resolution (LR) HSI with a multispectral image (MSI) whose spatial resolution is higher than the HSI. In this article, we proposed a novel adaptive nonnegative sparse representation-based model to fuse an HSI and its corresponding MSI. First, basing the linear spectral unmixing, the nonnegative structured sparse representation model estimates the sparse codes of the desired high-resolution HSI from both the LR-HSI and the MSI. Then, the adaptive sparse representation can balance the relationship between the sparsity and collaboration by generating a suitable coefficient. Finally, in order to obtain more accurate results, we alternately optimize the spectral basis and coefficients rather than keeping the spectral basis fixed. The alternating direction method of multipliers is applied to solve the proposed optimization problem. The experimental results on both ground-based HSIs and real remote sensing HSIs show the superiority of our proposed approach to some other state-of-the-art HSI super-resolution methods.

Multi-structure non-linear semi-parametric optimal weighting parameters estimation algorithm based on adaptive sparse representation deconstruction model and integrative COD comprehensive model

For the combined orbit determination (COD) satellite-network system which is made of GEO-IGSO-MEO satellite-constellation and LEO satellites, the satellites-trajectory are not precise enough, because the meticulous satellite dynamics model, credible observation data model and optimal parameters estimation method are not taking into consideration. The high-level adaptive sparse-deconstruction dynamics model, non-linear semi-parametric combined orbit determination comprehensive model is put forward. By using the above models, adaptive weighting iterative wavelets decomposition algorithm of sparse-representation deconstruction model and multi-structure non-linear semi-parametric optimization weighted estimation algorithm of integrative combined orbit determination comprehensive models are designed. Simulated calculation results show that results showed that the high-precision sparse representation method and the optimization model estimation method can improve COD precision, and when comprehensive model systematic & random error parameters and adaptive sparse-representation deconstruction model parameters are estimated by integrative nonlinear semi-parametric comprehensive model synchronously, COD ultimate trajectory calculation estimation precision can get significant improvement.