Linear Feature Extraction Algorithms Research Articles

A LiDAR point cloud registration method combining linear feature extraction and TrICP algorithm

A LiDAR point cloud registration method combining linear feature extraction and TrICP algorithm

Calibration transfer for near-infrared (NIR) spectroscopy based on local preserving projection

Calibration transfer is a mathematical or statistical technique to solve the universality of near-infrared (NIR) spectroscopy, which is particularly important in practical applications. Therefore, a calibration transfer method based on local preserving projection (CTLPP) to correct spectral differences was proposed in this paper. Local preserving projection (LPP) is a linear feature extraction algorithm, which is a linear approximation of Laplacian Eigenmap manifold learning. It can not only overcome the shortcomings of linear feature extraction algorithms which are difficult to maintain the nonlinear manifold of the original data, but also solve the problem that nonlinear manifold learning methods are difficult to directly map new samples, which is conducive to building the conversion relationship between the source spectra and the target spectra. The transfer performance of the proposed method was evaluated in three datasets, and compared with five approaches, namely, piecewise direct standardization (PDS), calibration transfer based on independent component analysis (CTICA), spectral space transformation (SST), principal components canonical correlation analysis (PC-CCA) and calibration transfer based on neighborhood preserving embedding (CTNPE). Experimental results indicated that this proposed method can successfully correct the detection spectra among different apparatus. In addition, it can obtain relatively low prediction root mean square error (RMSEP). Therefore, the comprehensive research conducted in this work demonstrated that the proposed method is a promising calibration transfer method in NIR applications, which can provide a reference when sharing the NIR spectral models with more and more instruments.

Welding Quality Detection for Variable Groove Weldments Based on Infrared Sensor and Artificial Neural Network

Connecting a variable groove weldment is always challenging, and it is necessary to monitor the course of the work and optimize the welding process parameters in real time to ensure the final welding forming quality. Welding penetration is an important index to appraise the welding forming quality; the visual sensing method for molten pool is the main method for detecting the weld penetration, but its detection accuracy is affected by the arc light. In this paper, a welding penetration sensing method for variable groove weldments based on the welding temperature field distribution is proposed. Firstly, a set of temperature field measurement system for a weldment is developed by means of an infrared sensor. Secondly, in the direction perpendicular to the welding direction, a linear temperature distribution feature extraction algorithm based on Gaussian fitting is studied; in the direction parallel to the welding direction, the linear temperature distribution feature extraction algorithm based on the thermal cycle parameters is studied, and the feasibility of using the extracted linear temperature distribution features to identify the weld penetration of a variable groove weldment is analyzed. Finally, taking the extracted linear temperature distribution features as input, using an artificial neural network, the prediction model for the welding penetration of a variable groove weldment is established. The experimental results showed that the weld penetration sensing method put forward in this paper can realize high-precision weld penetration sensing and has high reliability, which solves the problem that weld penetration sensing is affected by arc light to a great extent.

New Application of Life Rank Algorithm: A Case Study

The past few years have seen the emergence of learning-to-rank (LTR) in the field of machine learning. In information acquiring the size of data is very large and empowering a learning-to-rank model on it will be a costly and time taking process. High dimension data leads to irrelevant and redundant data which results in overfitting. “Dimensionality reduction” methods are used to manage this issue. There are two-dimensionality reduction techniques namely feature selection and feature reduction. There is extensive research available on the algorithm for learning-to-rank but this not the case for dimensionality reduction approaches in LTR, despite its importance. Feature selection techniques for classification are directly used for ranking. To the best of our understanding, feature extraction techniques in the context of ranking problems are not explored much to date. So, we make an effort to fill this void and explore feature extraction in the context of LTR problems. The LifeRank algorithm is a linear feature extraction algorithm for ranking. Its performance is analyzed on RankSVM and Linear regression. It is not applied to other learning-to-rank algorithms. So, in this task, an attempt is made to study the effect of the application of the LifeRank algorithm on other LTR algorithms. LifeRank algorithm is applied on RankNet and RankBoost. Then, the performance of several LTR algorithms on the LETOR dataset is analyzed before and after feature extraction.

Speech recognition in flight simulator

The article is devoted to the investigation of speech recognition in Flight Simulator cockpit. We have done research and developed software for speech recognition in Flight Simulator with limited vocabulary in C# from scratch. The speech recognition system works in real time and offline mode in Windows platform. We used Mel Frequency Cepstral Coefficients and Linear Predictive Coefficients feature extraction algorithms and trained the system by multilayer artificial neural networks. The User Interface of platform is highly functional and allows users to update system parameters through the interface. Our Speech Recognition system results are compared to the results of Microsoft Speech SDK and received satisfactory achievement.

Multi-layer Contribution Propagation Analysis for Fault Diagnosis

The recent development of feature extraction algorithms with multiple layers in machine learning and pattern recognition has inspired many applications in multivariate statistical process monitoring. In this work, two existing multilayer linear approaches in fault detection are reviewed and a new one with extra layer is proposed in analogy. To provide a general framework for fault diagnosis in succession, this work also proposes the contribution propagation analysis which extends the original definition of contribution of variables in multivariate statistical process monitoring. In fault diagnosis stage, the proposed contribution propagation analysis for multilayer linear feature extraction algorithms is compared with the fault diagnosis results of original contribution plots associated with single layer feature extraction approach. Plots of variable contributions obtained by the aforementioned approaches on the data sets collected from a simulated benchmark case study (Tennessee Eastman process) as well as an industrial scale multiphase flow facility are presented as a demonstration of the usage and performance of the contribution propagation analysis on multilayer linear algorithms.

Supervised locally linear embedding for magnetic resonance imaging based Alzheimer's disease classification

In order to solve the problem of early classification of Alzheimer's disease (AD), the conventional linear feature extraction algorithm is difficult to extract the most discriminative information from the high-dimensional features to effectively classify unlabeled samples. Therefore, in order to reduce the redundant features and improve the recognition accuracy, this paper used the supervised locally linear embedding (SLLE) algorithm to transform multivariate data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions. The 412 individuals were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including stable mild cognitive impairment (sMCI, n = 93), amnestic mild cognitive impairment (aMCI, n = 96), AD ( n = 86) and cognitive normal controls (CN, n = 137). The SLLE algorithm used in this paper is to calculate the nearest neighbors of each sample point by adding the distance correction term, and the locally linear reconstruction weight matrix was obtained from its nearest neighbors, then the low dimensional mapping of the high dimensional data can be calculated. In order to verify the validity of SLLE in the task of classification, the feature extraction algorithms such as principal component analysis (PCA), Neighborhood MinMax Projection (NMMP), locally linear mapping (LLE) and SLLE were respectively combined with support vector machines (SVM) classifier to obtain the accuracy of classification of CN and sMCI, CN and aMCI, CN and AD, sMCI and aMCI, sMCI and AD, and aMCI and AD, respectively. Experimental results showed that our method had improvements (accuracy/sensitivity/specificity: 65.16%/63.33%/67.62%) on the classification of sMCI and aMCI by comparing with the combination algorithm of LLE and SVM (accuracy/sensitivity/specificity: 64.08%/66.14%/62.77%) and SVM (accuracy/sensitivity/specificity: 57.25%/56.28%/58.08%). In detail the accuracy of the combination algorithm of SLLE and SVM is 1.08% higher than the combination algorithm of LLE and SVM, and 7.91% higher than SVM. Thus, the combination of SLLE and SVM is more effective in the early diagnosis of Alzheimer's disease.

Linear feature extraction for ranking

We address the feature extraction problem for document ranking in information retrieval. We then propose LifeRank, a Linear feature extraction algorithm for Ranking. In LifeRank, we regard each document collection for ranking as a matrix, referred to as the original matrix. We try to optimize a transformation matrix, so that a new matrix (dataset) can be generated as the product of the original matrix and a transformation matrix. The transformation matrix projects high-dimensional document vectors into lower dimensions. Theoretically, there could be very large transformation matrices, each leading to a new generated matrix. In LifeRank, we produce a transformation matrix so that the generated new matrix can match the learning to rank problem. Extensive experiments on benchmark datasets show the performance gains of LifeRank in comparison with state-of-the-art feature selection algorithms.

Fusion of lattice independent and linear features improving face identification

This paper proposes the fusion of lattice independent component analysis (LICA) features with linear features obtained from conventional methods. Specifically, we compute class conditional LICA, where separate endmembers are extracted from each class dataset. We find that this fusion approach improves systematically the recognition accuracy in face recognition applications. We report experimental results using seven state-of-the-art linear feature extraction algorithms on four public face databases using Extreme Learning Machines (ELMs) for the classification building algorithm.

Linear and Non-Linear Feature Extraction Algorithms for Lunar Images

Linear and Non-Linear Feature Extraction Algorithms for Lunar Images

Linear and Non-Linear Feature Extraction Algorithms for Lunar Images

Linear and Non-Linear Feature Extraction Algorithms for Lunar Images

Potential of using automatically extracted straight lines in rectifying high-resolution satellite images

Rapid advances in remote-sensing technologies have provided the ability to acquire high-quality metric information. This is achieved by forming a relationship between image coordinates and ground coordinates as two-dimensional (2D) and 2D-to-3D transformation models. Most of these models are based on point features. Although point-based transformation models are well developed, it is time-consuming and costly to measure point coordinates manually. However, automatic point extraction algorithms, especially in satellite images, are far inferior in quality. This makes other feature-based transformation models more attractive. Linear features have greater potential than other features not only in automation but also in representation and modelling. In this study we investigated rectifying satellite images with different resolutions and orientations using line-based transformation models (LBTMs). In addition, the relative relationships between linear features were imposed by incorporating geometric constraints. Straight lines were extracted using an automated linear feature extraction algorithm. Experiments were conducted with different sets of straight lines and their results are reported and analysed. Our research reveals that LBTMs provide equivalent results to those obtained using point-based transformation models, especially when geometric constraints are forced. Combined point/line-based transformation models were also investigated and the results are similar to those achieved with either point or line features alone.

Orthogonal locality minimizing globality maximizing projections for feature extraction

Locality preserving projections (LPP) is a recently developed linear-feature extraction algorithm that has been frequently used in the task of face recognition and other applications. However, LPP does not satisfy the shift-invariance property, which should be satisfied by a linear-feature extraction algorithm. In this paper, we analyze the reason and derive the shift-invariant LPP algorithm. Based on the analysis of the geometrical meaning of the shift-invariant LPP algorithm, we propose two algorithms to minimize the locality and maximize the globality under an orthogonal projection matrix. Experimental results on face recognition are presented to demonstrate the effectiveness of the proposed algorithms.

On Feature Extraction via Kernels

Using the kernel trick idea and the kernels-as-features idea, we can construct two kinds of nonlinear feature spaces, where linear feature extraction algorithms can be employed to extract nonlinear features. In this correspondence, we study the relationship between the two kernel ideas applied to certain feature extraction algorithms such as linear discriminant analysis, principal component analysis, and canonical correlation analysis. We provide a rigorous theoretical analysis and show that they are equivalent up to different scalings on each feature. These results provide a better understanding of the kernel method.

Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition

Feature extraction is an important component of a pattern recognition system. It performs two tasks: transforming input parameter vector into a feature vector and/or reducing its dimensionality. A well-defined feature extraction algorithm makes the classification process more effective and efficient. Two popular methods for feature extraction are linear discriminant analysis (LDA) and principal component analysis (PCA). In this paper, the minimum classification error (MCE) training algorithm (which was originally proposed for optimizing classifiers) is investigated for feature extraction. A generalized MCE (GMCE) training algorithm is proposed to mend the shortcomings of the MCE training algorithm. LDA, PCA, and MCE and GMCE algorithms extract features through linear transformation. Support vector machine (SVM) is a recently developed pattern classification algorithm, which uses non-linear kernel functions to achieve non-linear decision boundaries in the parametric space. In this paper, SVM is also investigated and compared to linear feature extraction algorithms.

Road extraction from aerial and satellite images by dynamic programming

In this paper, we propose a semi-automatic road extraction scheme which combines the wavelet decomposition for road sharpening and a model-driven linear feature extraction algorithm based on dynamic programming. Semi-automatic means that a road is extracted automatically after some seed points have been given coarsely by the operator through activation of a mouse using a convenient interactive image-graphics user interface. With a wavelet transform interesting image structures can be enhanced and a multiresolution representation can be obtained by selection of a special wavelet. We have built a special wavelet for road sharpening, which has been implemented as a fast pyramidal algorithm. In the model-driven feature extraction scheme, a road is represented by a generic road model with six photometric and geometric properties. This model is formulated by some constraints and a merit function which embodies a notion of the “best road segment”, and evaluated by a “time-delayed” dynamic programming algorithm. The mathematical foundation and issues relating to its practical implementation are discussed in detail. This approach has been applied very successfully to extract complete road networks from single SPOT scenes and aerial images. Thereby the algorithm runs in a monoplotting mode, deriving X, Y, Z-coordinates of the roads, whereby the Z-component comes from real-time interpolation within an underlying DTM. Some experimental results are also given in this paper.

An algorithm for optimal single linear feature extraction from several gaussian pattern classes

A computational algorithm is presented for the extraction of an optimal single linear feature from several Gaussian pattern classes. The algorithm minimizes the increase in the probability of misclassification in the transformed (feature) space. The general approach used in this procedure was developed in a recent paper by R. J. P. de Figueiredo.(1) Numerical results on the application of this procedure to the remotely sensed data from the Purdue C1 flight line as well asLandsat data are presented. It was found that classification using the optimal single linear feature yielded a value for the probability of misclassification on the order of 30% less than that obtained by using the best single untransformed feature. The optimal single linear feature gave performance results comparable to those obtained by using the two features which maximized the average divergence. Also discussed are improvements in classification results using this method when the size of the training set is small.