Fisher Discrimination Dictionary Learning Research Articles

Fisher discrimination multiple kernel dictionary learning for robust identification of nonlinear features in machinery health monitoring

In sparse representation-based classification, Fisher discrimination dictionary learning (FDDL) has attracted widespread attention due to its advantages such as fewer parameters and good dictionary discriminability. However, due to its linear nature, it is difficult to handle nonlinear features. Kernel-based nonlinear transformation enables dictionary learning to capture nonlinear features embedded in samples, but traditional single-kernel methods have limited performance. In this paper, a Fisher discrimination multiple kernel dictionary learning (FDMKDL) method is proposed, in which Fisher discriminative criterion is imposed on both high-dimensional samples and coding coefficients. Specifically, we derive the kernel version of FDDL, i.e., Fisher discrimination kernel dictionary learning (FDKDL) to learn nonlinear features and promote dictionary discriminability. Meanwhile, to avoid the problem of poor adaptability and possible manual selection caused by single-kernel methods, we further derive a flexible multiple kernel learning (MKL) framework, which utilizes the complementary information of multiple kernel functions to adaptively obtain the optimal weights and synthesize a discriminative kernel space. Finally, FDMKDL combines FDKDL with this MKL framework to obtain a more discriminative dictionary. Experimental evaluations conducted on two datasets demonstrate the effectiveness and robustness of the proposed FDMKDL method in learning nonlinear features for machinery health monitoring when compared to several state-of-the-art methods.

Active Sonar Target Classification Method Based on Fisher’s Dictionary Learning

The marine environment is complex and changeable, and the interference of noise and reverberation seriously affects the classification performance of active sonar equipment. In particular, when the targets to be measured have similar characteristics, underwater classification becomes more complex. Therefore, a strong, recognizable algorithm needs to be developed that can handle similar feature targets in a reverberation environment. This paper combines Fisher’s discriminant criterion and a dictionary-learning-based sparse representation classification algorithm, and proposes an active sonar target classification method based on Fisher discriminant dictionary learning (FDDL). Based on the learning dictionaries, the proposed method introduces the Fisher restriction criterion to limit the sparse coefficients, thereby obtaining a more discriminating dictionary; finally, it distinguishes the category according to the reconstruction errors of the reconstructed signal and the signal to be measured. The classification performance is compared with the existing methods, such as SVM (Support Vector Machine), SRC (Sparse Representation Based Classification), D-KSVD (Discriminative K-Singular Value Decomposition), and LC-KSVD (label-consistent K-SVD), and the experimental results show that FDDL has a better classification performance than the existing classification methods.

Optimized Projection and Fisher Discriminative Dictionary Learning for EEG Emotion Recognition

Electroencephalogram (EEG)-based emotion recognition (ER) has drawn increasing attention in the brain–computer interface (BCI) due to its great potentials in human–machine interaction applications. According to the characteristics of rhythms, EEG signals usually can be divided into several different frequency bands. Most existing methods concatenate multiple frequency band features together and treat them as a single feature vector. However, it is often difficult to utilize band-specific information in this way. In this study, an optimized projection and Fisher discriminative dictionary learning (OPFDDL) model is proposed to efficiently exploit the specific discriminative information of each frequency band. Using subspace projection technology, EEG signals of all frequency bands are projected into a subspace. The shared dictionary is learned in the projection subspace such that the specific discriminative information of each frequency band can be utilized efficiently, and simultaneously, the shared discriminative information among multiple bands can be preserved. In particular, the Fisher discrimination criterion is imposed on the atoms to minimize within-class sparse reconstruction error and maximize between-class sparse reconstruction error. Then, an alternating optimization algorithm is developed to obtain the optimal solution for the projection matrix and the dictionary. Experimental results on two EEG-based ER datasets show that this model can achieve remarkable results and demonstrate its effectiveness.

Low-dimensional multi-scale Fisher discriminant dictionary learning for intelligent gear-fault diagnosis

Fisher discriminant dictionary learning (FDDL) is an effective classification method that has achieved excellent results in image processing. However, there are still some shortcomings when it is directly applied to intelligent gear-fault diagnosis, such as low classification accuracy, a time-consuming dictionary learning process and unsatisfactory anti-interference capabilities, especially for vibration signals with heavy noise. To solve these problems, this paper proposes a low-dimensional multi-scale FDDL (LM-FDDL) model, which extracts the fault-sensitive and multi-scale information from wavelet packet transform coefficients, and constructs a low-dimensional sample set via L-kurtosis for dictionary learning. This improves the anti-interference capability of the model and greatly reduces the computational cost. The dictionary learned from the low-dimensional sample set has excellent discriminant performance and interpretability, which are verified by the visualization of reconstructed signals. Experimental results for a spur-gear fault dataset and a bevel-gear fault dataset demonstrate the superiority of LM-FDDL over other related methods in terms of fault classification accuracy and computational cost. An added-noise comparison experiment verifies that the dimension -reducing strategy of LM-FDDL has strong robustness to noise and outliers, and thus ensures that the main fault features can be extracted from vibration signals with heavy noise interference.

Load-Identification Method for Flexible Multiple Corrugated Skin Using Spectra Features of FBGs

Flexible corrugated skins are ideal structures for morphing wings, and the associated load measurements are of great significance in structural health monitoring. This paper proposes a novel load-identification method for flexible corrugated skins based on improved Fisher discrimination dictionary learning (FDDL). Several fiber Bragg grating sensors are pasted on the skin to monitor the load on multiple corrugated crests. The loads on different crests cause nonuniform strain fields, and these discriminative spectra are recorded and used as training data. The proposed method involves load-positioning and load-size identification. In the load-size-identification stage, a classifier is trained for every corrugated crest. An interleaved block grouping of samples is introduced to enhance the discrimination of dictionaries, and a two-resolution load-size classifier is introduced to improve the performance and resolution of the grouping labels. An adjustable weight is introduced to the FDDL classification scheme to optimize the contribution from different sensors for different load-size classifiers. With the proposed method, the individual loads on eight crests can be identified by two fiber Bragg grating sensors. The positioning accuracy is 100%, and the mean error of the load-size identification is 0.2106 N, which is sufficiently precise for structural health monitoring.

Online Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation

The nonlinear output characteristics of PV arrays and maximum power point tracking (MPPT) techniques bring more difficulties to fault diagnosis. The fault diagnosis model based on electrical transient time-domain analysis is an effective method for solving the above problems. However, existing studies using transient processes usually train their models by extensive labeled datasets, and some approaches apply normalization methods with environmental condition sensors or reference PV panels. Therefore, Fisher discrimination dictionary learning (FDDL) for sparse representation is explored for diagnosing PV array faults, including line-to-line faults (LLF), open-circuit faults (OCF), and partial shading faults (PSF), with a small labeled dataset, and a dynamic normalization method without additional sensors is proposed to process transient data. Moreover, LLF and PSF that have similar characteristics under low mismatch should be further distinguished. The proposed model is designed with two stages. In the first stage, a multiple classifier trained using small labeled datasets with all fault types is applied to diagnose all kinds of studied PV array faults. Then, a dictionary only for PSF and LLF is learned in the second stage to further identify LLF and PSF. Finally, a 1.8 kW rooftop grid-connected PV system with $6\times3$ PV arrays is applied to validate the performance of the proposed model. The comparison result shows the superiority of the proposed model.

Single-Channel sEMG Dictionary Learning Classification of Ingestive Behavior on Cows

Monitoring the ingestive behavior, especially rumination and eating, is important to assess animals’ health, welfare, needs, and to estimate production efficiency. In this paper, the myographic response of masticatory muscles was acquired from cows to directly access ingestive information (grazing and ruminating patterns) with high accuracy and time precision. Myographic signal processing is usually based on the same framework and optimal feature combinations have been extensively researched. However, recognition rates of hand-crafted feature based classifiers may be sensible to data deviation. In order to avoid the aforementioned issues and improve robustness, we propose an approach using sparse representation based classifiers which dismiss the need to select the best feature combination and classifier for the application. Also, we present a novel segmentation protocol to select chew related signal windows on cows. Four state-of-the-art multi-feature sets combined with linear discriminant analysis (LDA) were compared to a sparse representation based classifier called Fisher Discriminant Dictionary Learning (FDDL). Results suggest a significantly better performance of FDDL (p<0.05), achieving an accuracy rate of 90%, even in the presence of noise (0 – 20 dB), having a performance gain of 2.45% (SNR of 0 dB) in relation to the best hand-crafted feature set. When the noise was added just to the test set, all others classifiers remained under 75% (near 50%) for severe noise (0 – 10 dB), while FDDL stayed over 85%.

Optimal decision fusion using sparse representation‐based classifiers on monogenic‐signal dictionaries for SAR ATR

Sparse representation-based classification (SRC) possesses remarkable characteristics for application in synthetic aperture radar (SAR) automatic target recognition (ATR), for instance, inherent feature extraction and robustness to articulation, and so on. However, the performance of SRC is highly sensitive to parameters such as sufficient training samples, SAR images quality, and targets’ changing conditions in depression, pose, configuration, and so on. Unfortunately, the training sample resources for SAR ATR are often expensive and scarce. Further, the targets in SAR images, even with slightest changes in conditions, display mutable characteristics attributable to unique SAR image formation, and speckle noise corruption. To overcome these obstacles, this Letter proposes to establish several compact and complementary dictionaries using monogenic signal's components of SAR images and Fisher discriminative dictionary learning. Then, an optimal decision fusion (ODF) strategy is proposed, which utilises SRC and the latter dictionaries for robust SAR ATR. Compared with single classifiers or multiple parallel classifiers, the proposed ODF increases the accuracy of recognition, while at the same time, decreases the complexity of the system. The proposed methods have considerable features making them applicable in practical situations. Based on the experimental results, the proposed methods outperform state-of-the-art approaches.

Discriminative-shared dictionary learning for class-specific fabric texture characterization

In this paper we introduced a novel discriminative-shared dictionary learning (DSDL) model to explicitly extract a set of class-specific features as well as shared features for simultaneous fabric texture characterization. For the discriminative component, we imposed the constraints of minimizing inter-class correlation as well as maximizing intra-class correlation on them. For the shared component, we enforced a low-rank constraint and a similarity constraint on it. To demonstrate the characterization performance of the learned dictionary on multi-class fabric textures, we weaved eight fabric textures in the laboratory and then reconstructed each class of sample with each class of specific dictionary. Preliminary experiments with four-class samples demonstrate that the specific class of dictionary can only reconstruct the corresponding class of fabric texture, but has weak reconstructive ability for the rest class, and vice versa. In addition, we illustrated the contribution of shared dictionary by comparing the convergence rate of DSDL with that of Fisher Discriminative Dictionary Learning. Further, we developed a new indicator based on a similarity matrix for evaluating the discriminative of class-specific dictionaries, and we validated the effectiveness of the indicator by comparing the discriminative indicators of eight group sets. In general, the proposed DSDL model can effectively extract discriminative features and shared features simultaneously of multi-class fabric textures.

Decision fusion using virtual dictionary‐based sparse representation for robust SAR automatic target recognition

Sparse representation displays remarkable characteristics when applied to image processing and classification. The critical point in the success of sparse representation-based classification is to learn an authentic dictionary. The present study proposes a virtual dictionary-based sparse representation for automatic target recognition. Based on the properties of the synthetic aperture radar (SAR) images, some low complexity modules including adding speckle noise, histogram equalisation mapping, and bicubic interpolation are applied to construct some virtual compact dictionaries using Fisher discriminative dictionary learning. These dictionaries have different discriminative information on targets, which are used independently in several sparse representation-based classifiers. The reconstruction error vectors of the latter classifiers are then combined to recognise the target using decision fusion. Based on experimental results obtained drawing upon moving and stationary target acquisition and recognition data set, the proposed method presents the highest accuracy in classification reported yet in the literature. Furthermore, the procedure improves the recognition robustness against most commonly extended operating conditions, e.g. speckle noise corruption, depression angle variation and reduced training set. Accordingly, the current study claims a robust parallel method of high real-time ability in the target recognition of SAR images applicable to practical situations.

Multi-task hybrid dictionary learning for vehicle classification in sensor networks

In this article, we propose a novel multi-task hybrid dictionary learning approach for moving vehicle classification tasks using multi-sensor networks to improve the classification accuracy in complex scenes with low time complexity, which considers both correlations and complementary information among multiple heterogeneous sensors simultaneously to learn a hybrid dictionary within observations of each sensor. The efficient hybrid dictionary consists of a synthesis dictionary and an analysis dictionary, where discriminative codes can be generated by the trained analysis dictionary and class-specific discriminative reconstruction can be achieved by the trained synthesis dictionary. Extensive experiments are conducted on real data sets captured by the multiple heterogeneous sensors, and the results demonstrate that the proposed method can use the multi-feature fusion method to improve the vehicle classification accuracy, and it can learn a hybrid dictionary to make sure that the sparse coding matrix is obtained by simple linear mapping function. Moreover, the problem of [Formula: see text]-norm[Formula: see text] sparse coding can been solved, to reduce the time complexity of this algorithm, compared with support vector machine, sparse representation classification, label consistent KSVD, Fisher discrimination dictionary learning, hybrid dictionary learning, multi-task sparse representation classification, and multi-task Fisher discrimination dictionary learning algorithms.

Fisher Discrimination Regularized Robust Coding Based on a Local Center for Tumor Classification

Tumor classification is crucial to the clinical diagnosis and proper treatment of cancers. In recent years, sparse representation-based classifier (SRC) has been proposed for tumor classification. The employed dictionary plays an important role in sparse representation-based or sparse coding-based classification. However, sparse representation-based tumor classification models have not used the employed dictionary, thereby limiting their performance. Furthermore, this sparse representation model assumes that the coding residual follows a Gaussian or Laplacian distribution, which may not effectively describe the coding residual in practical tumor classification. In the present study, we formulated a novel effective cancer classification technique, namely, Fisher discrimination regularized robust coding (FDRRC), by combining the Fisher discrimination dictionary learning method with the regularized robust coding (RRC) model, which searches for a maximum a posteriori solution to coding problems by assuming that the coding residual and representation coefficient are independent and identically distributed. The proposed FDRRC model is extensively evaluated on various tumor datasets and shows superior performance compared with various state-of-the-art tumor classification methods in a variety of classification tasks.

Multi-task Joint Sparse Representation Classification Based on Fisher Discrimination Dictionary Learning

Recently, sparse representation classification (SRC) and fisher discrimination dictionary learning (FDDL) methods have emerged as important methods for vehicle classification. In this paper, inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection, we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors. To improve the classification accuracy in complex scenes, we develop a new method, called multi-task joint sparse representation classification based on fisher discrimination dictionary learning, for vehicle classification. In our proposed method, the acoustic and seismic sensor data sets are captured to measure the same physical event simultaneously by multiple heterogeneous sensors and the multi-dimensional frequency spectrum features of sensors data are extracted using Mel frequency cepstral coefficients (MFCC). Moreover, we extend our model to handle sparse environmental noise. We experimentally demonstrate the benefits of joint information fusion based on fisher discrimination dictionary learning from different sensors in vehicle classification tasks.

Marginalized Denoising Dictionary Learning With Locality Constraint.

Learning good representation for images is always a hot topic in machine learning and pattern recognition fields. Among the numerous algorithms, dictionary learning is a well-known strategy for effective feature extraction. Recently, more discriminative sub-dictionaries have been built by Fisher discriminative dictionary learning with specific class labels. Different types of constraints, such as sparsity, low rankness, and locality, are also exploited to make use of global and local information. On the other hand, as the basic building block of deep structure, the auto-encoder has demonstrated its promising performance in extracting new feature representation. To this end, we develop a unified feature learning framework by incorporating the marginalized denoising auto-encoder into a locality-constrained dictionary learning scheme, named marginalized denoising dictionary learning. Overall, we deploy low-rank constraint on each sub-dictionary and locality constraint instead of sparsity on coefficients, in order to learn a more concise and pure feature spaces meanwhile inheriting the discrimination from sub-dictionary learning. Finally, we evaluate our algorithm on several face and object data sets. Experimental results have demonstrated the effectiveness and efficiency of our proposed algorithm by comparing with several state-of-the-art methods.

Learning Discriminative Dictionary for Facial Expression Recognition

ABSTRACTSparse coding is currently an active subject in signal processing, computer vision, pattern recognition, etc. Fisher discrimination dictionary learning (FDDL) is a recently developed discriminative dictionary learning method and exhibits promising performance for classification. However, FDDL could not capture the locality structure of data, and it produces discriminative sparse coding coefficients, which is not effective enough for classification. To address these issues, this paper proposes an advanced version of FDDL by integrating data locality and group Lasso regularization in the procedure of FDDL's sparse coding. The proposed method is used to learn locality- and group-sensitive discriminative dictionary for facial expression recognition. Our experimental results on two public facial expression databases, i.e., the JAFFE database and the Cohn–Kanade database, demonstrate the effectiveness of the proposed method on facial expression recognition tasks, giving a significant performance improvement over FDDL.

A Simplified FDDL Algorithm for Face Recognition

In this paper, a modified Fisher Discrimination Dictionary Learning method is introduced, which is suitable for face recognition without and with disguise. In the proposed method the new optimization model is establish, so as to preserve intrinsic complementary penalty as well as able to simplify calculation of original algorithm. The effective and efficient of the proposed method is demonstrated experimentally by considering the YALE, ORL, and AR face database.

Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis

Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.

Fast Low-Rank Shared Dictionary Learning for Image Classification.

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e., claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Furthermore, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image data sets establish the advantages of our method over the state-of-the-art dictionary learning methods.

Unsupervised dictionary learning with Fisher discriminant for clustering

In this paper, we propose a novel Fisher discriminant unsupervised dictionary learning (FD-UDL) approach, for improving the clustering performance of state-of-the-art dictionary learning approaches in unsupervised scenarios. This is achieved by employing a novel Fisher discriminant criterion on dictionary elements to encourage the diversity between different sub-dictionaries, and also the coherence within each sub-dictionary. Such a discriminant is incorporated to formulate the optimization problem of unsupervised dictionary learning. Furthermore, we provide an analytical solution to the proposed optimization problem, obtaining the learned dictionary for clustering tasks. Unlike previous approaches for unsupervised clustering, the proposed FD-UDL approach takes into account both within-class and between-class scatters of sub-dictionaries, rather than only considering diversity between different sub-dictionaries. Finally, experiments on synthetic data, face and handwritten digit clustering tasks show the improved clustering accuracy over other state-of-the-art dictionary learning and clustering approaches.

Fisher Discriminative Dictionary Learning for Vehicle Classification in Acoustic Sensor Networks

In this paper, we propose a discriminative dictionary learning framework for vehicle classification to improve the classification accuracy, and study the fisher discriminative dictionary learning (FDDL) approach in acoustic sensor networks. More precisely, the acoustic sensor data sets are captured to measure the vehicle running event. The multi-dimensional frequency spectrum features of sensor data sets are extracted using Mel frequency cepstral coefficients (MFCC), and the vehicle classification scheme is solved using fisher discriminative dictionary learning method, which exploits the discriminative information in both the representation residuals and the representation coefficients. To further analyze the performance of our proposed model, we extend our model to deal with sparse environmental noise. Extensive experiments are conducted on acoustic sensor databases and the results demonstrate that our proposed model shows superior performance in this vehicle classification framework compared to SVM, SRC, KSRC and LC-KSVD algorithms.