Discriminative Dictionary Research Articles

Fusion of effective dimension reduction and discriminative dictionary learning for high-dimensional classification

High-dimensional classification is a hot topic of pattern recognition. However, its performance has much room for improvement due to environment uncertainties and other factors. For example, redundant information from high dimensional data, illumination, and occlusion may lead to the inferior classification performance. Additionally, some potentially useful features of high-dimensional data are not fully captured. To address these problems, we propose a fusion of effective dimension reduction and discriminative dictionary learning for high-dimensional classification. A novel dimension reduction approach is employed to map training samples into a low-dimensional space by using the distance and label information. Then in the low-dimensional subspace, we integrate atomic Fisher constraint and local constraint of coding coefficients into the dictionary learning. Thus the discriminative atoms are effectively extracted. Finally, experimental results prove the efficacy of the proposed model. Besides, the comparative experiments with AlexNet and VGG19 prove the superiority of our method over some deep learning approaches.

Meta-Pixel-Driven Embeddable Discriminative Target and Background Dictionary Pair Learning for Hyperspectral Target Detection

In hyperspectral target detection, the spectral high-dimensionality, variability, and heterogeneity will pose great challenges to the accurate characterizations of the target and background. To alleviate the problems, we propose a Meta-pixel-driven Embeddable Discriminative target and background Dictionary Pair (MEDDP) learning model by combining low-dimensional embeddable subspace projection and the discriminative target and background dictionary pair learning. In MEDDP, the meta-pixel set is built by taking the merits of homogeneous superpixel segmentation and the local manifold affinity structures, which can significantly reduce the influence of spectral variability and find the most typical and informative prototype spectral signature. Afterward, an embeddable discriminative dictionary pair learning model is established to learn a target and background dictionary pair based on the structural incoherent constraint with embeddable subspace projection. The proposed joint learning strategy can reduce the high-dimensional redundant information and simultaneously enhance the discrimination and compactness of the target and background dictionaries. The proposed MEDDP model is solved by an iterative and alternate optimization algorithm and applied with the meta-pixel-level target detection method. Experimental results on four benchmark HSI datasets indicate that the proposed method can consistently yield promising performance in comparison with some state-of-the-art target detectors.

A novel discriminative dictionary learning method for image classification

A novel discriminative dictionary learning method for image classification

Hierarchical Dictionary Learning for Vehicle Classification Based on the Carrier-Free UWB Radar

As a promising technique, dictionary learning (DL) for target recognition has seen a recent surge in recent years. Although many methods have been proposed to obtain discriminative dictionaries or coefficients via incorporating various constraints into the objective function, there are still two issues. First, it is well known that kinds of discriminative criteria on the objective function often involve substantial optimized items, increasing computation cost. Second, noises in the real world inevitably degrade the classification performance, while most DL algorithms disregard that. Aiming at these two problems, a hierarchical DL model is proposed for vehicle recognition based on the carrier-free ultrawideband (UWB) radar. With the purpose of successfully determining the identity of targets, we first learn several class-specific subdictionaries. Then, considering that the actual environment is filled with noises, we divided the learned dictionary atoms into signal and disturbance atoms in accordance with sparse coefficients to establish the signal dictionary and noise dictionary, respectively. Finally, the clean data are recovered over the corresponding signal dictionary, and meanwhile, the classification task is achieved. This hierarchical DL method takes into account both the noise-robust ability and discriminative power of the learned dictionary, in which the “atom selection” mechanism dramatically speeds up calculations. What is more, rather than imposing discriminative restraints on the objective function, we improve the K-SVD-based optimization process to complete hierarchical DL. Experimental results on the measured and synthetic data corroborate the effectiveness of the proposed method even under low signal-to-noise ratio (SNR) values. Especially, to testify to the generalization ability of the proposed method, we evaluate our algorithm on a public synthetic aperture radar (SAR) dataset (MSTAR).

DLDL: Dynamic label dictionary learning via hypergraph regularization

For classification tasks, dictionary learning based methods have attracted lots of attention in recent years. One popular way to achieve this purpose is to introduce label information to generate a discriminative dictionary to represent samples. However, compared with traditional dictionary learning, this category of methods only achieves significant improvements in supervised learning, and has little positive influence on semi-supervised or unsupervised learning. To tackle this issue, we propose a Dynamic Label Dictionary Learning (DLDL) algorithm to generate the soft label matrix for unlabeled data. Specifically, we employ hypergraph manifold regularization to keep the relations among original data, transformed data, and soft labels consistent. We demonstrate the efficiency of the proposed DLDL approach on two kinds of tasks, including remote sensing image classification and human activity recognition. The significant improvements have demonstrated the efficiency of our method.

Jointly Class-specific and Shared Discriminative Dictionary Learning for Classifying Surface Defects of Steel Sheet

Jointly Class-specific and Shared Discriminative Dictionary Learning for Classifying Surface Defects of Steel Sheet

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.

Cross-domain EEG signal classification via geometric preserving transfer discriminative dictionary learning

Cross-domain EEG signal classification via geometric preserving transfer discriminative dictionary learning

Learning a discriminative high-fidelity dictionary for single channel source separation

Sparse-representation-based single-channel source separation, which aims to recover each source's signal using its corresponding sub-dictionary, has attracted many scholars' attention. The basic premise of this model is that each sub-dictionary possesses discriminative information about its corresponding source, and this information can be used to recover almost every sample from that source. However, in a more general sense, the samples from a source are composed not only of discriminative information but also common information shared with other sources. This paper proposes learning a discriminative high-fidelity dictionary to improve the separation performance. The innovations are threefold. Firstly, an extra sub-dictionary was combined into a conventional union dictionary to ensure that the source-specific sub-dictionaries can capture only the purely discriminative information for their corresponding sources because the common information is collected in the additional sub-dictionary. Secondly, a task-driven learning algorithm is designed to optimize the new union dictionary and a set of weights that indicate how much of the common information should be allocated to each source. Thirdly, a source separation scheme based on the learned dictionary is presented. Experimental results on a human speech dataset yield evidence that our algorithm can achieve better separation performance than either state-of-the-art or traditional algorithms.

Sentiment Classification of News Text Data Using Intelligent Model

Text sentiment classification is a fundamental sub-area in natural language processing. The sentiment classification algorithm is highly domain-dependent. For example, the phrase “traffic jam” expresses negative sentiment in the sentence “I was stuck in a traffic jam on the elevated for 2 h.” But in the domain of transportation, the phrase “traffic jam” in the sentence “Bread and water are essential terms in traffic jams” is without any sentiment. The most common method is to use the domain-specific data samples to classify the text in this domain. However, text sentiment analysis based on machine learning relies on sufficient labeled training data. Aiming at the problem of sentiment classification of news text data with insufficient label news data and the domain adaptation of text sentiment classifiers, an intelligent model, i.e., transfer learning discriminative dictionary learning algorithm (TLDDL) is proposed for cross-domain text sentiment classification. Based on the framework of dictionary learning, the samples from the different domains are projected into a subspace, and a domain-invariant dictionary is built to connect two different domains. To improve the discriminative performance of the proposed algorithm, the discrimination information preserved term and principal component analysis (PCA) term are combined into the objective function. The experiments are performed on three public text datasets. The experimental results show that the proposed algorithm improves the sentiment classification performance of texts in the target domain.

Radioisotope identification using sparse representation with dictionary learning approach for an environmental radiation monitoring system

A radioactive isotope identification algorithm is a prerequisite for a low-resolution scintillation detector applied to an unmanned radiation monitoring system. In this paper, a sparse representation with dictionary learning approach is proposed and applied to plastic gamma-ray spectra. Label-consistent K-SVD was used to learn a discriminative dictionary for the spectra corresponding to a mixture of four isotopes (133Ba, 22Na, 137Cs, and 60Co). A Monte Carlo simulation was employed to produce the simulated data as learning samples. Experimental measurement was conducted to obtain practical spectra. After determining the hyper parameters, two dictionaries tailored to the learning samples were tested by varying with the source position and the measurement time. They achieved average accuracies of 97.6% and 98.0% for all testing spectra. The average accuracy of each dictionary was above 96% for spectra measured over 2 s. They also showed acceptable performance when the spectra were artificially shifted. Thus, the proposed method could be useful for identifying radioisotopes in gamma-ray spectra from a plastic scintillation detector even when a dictionary is adapted to only simulated data. Furthermore, owing to the outstanding properties of sparse representation, the proposed approach can easily be built into an in-situ monitoring system.

A sparse coded composite descriptor for human activity recognition

AbstractThis paper proposes a novel algorithm for computing discriminative descriptors named as a sparse coded composite descriptor (SCCD) for robust human activity recognition. The proposed method blends the state‐of‐the‐art handcrafted features and the discriminative nature of the sparse representation of visual information. The human activity is firstly modelled using any handcrafted feature, and then the sparse codes computed on a discriminative sparse dictionary of these features are embedded to provide discrimination in the feature set. Finally, a support vector machine (SVM) is trained using the proposed SCCDs to perform classification of different human activities. A new feature named as differential motion descriptor (DMD) is also proposed to extract the motion as well as spatial information from an activity video. The simulation results reveal that in comparison with the handcrafted feature, the corresponding SCCD improves the recognition accuracy significantly. The proposed method is compared with state‐of‐the‐art methods on KTH, Ballet, UCF50, and HMDB51 datasets and the proposed methodology of composite features outperforms these methods in terms of recognition accuracy.

Joint discriminative and shared dictionary learning with dictionary extension strategy for bearing fault classification

Sparse representation is one of the effective approaches for bearing fault diagnosis. Conventional sparse representation only focuses on fault feature extraction from bearing vibration signals while requiring experts to empirically make a diagnosis. This paper investigates bearing intelligent fault classification based on sparse representation based classification (SRC) method. A novel SRC algorithm, named joint discriminative and shared dictionary learning (JDSDL), is proposed for simultaneously characterize the class-specific and common features in vibration signals of bearings with different faults. The JDSDL seamlessly integrates discriminative dictionary learning, shared dictionary learning, and classifier training into a joint model to automatically recognize bearing faults. Moreover, a dictionary extension strategy is embedded within the proposed JDSDL model to make learned discriminative dictionary shift-invariant to improve its classification performance. The main advantages of the proposed method are that the JDSDL not only works well even if the training set is small, but also is robust to corruption of environment noises. Experimental results on three bearing datasets and comparison with competing methods are presented to demonstrate the effectiveness and superiority of the proposed JDSDL in bearing intelligent fault classification.

Single-channel speech enhancement based on joint constrained dictionary learning

To improve the performance of speech enhancement in a complex noise environment, a joint constrained dictionary learning method for single-channel speech enhancement is proposed, which solves the “cross projection” problem of signals in the joint dictionary. In the method, the new optimization function not only constrains the sparse representation of the noisy signal in the joint dictionary, and controls the projection error of the speech signal and noise signal on the corresponding sub-dictionary, but also minimizes the cross projection error and the correlation between the sub-dictionaries. In addition, the adjustment factors are introduced to balance the weight of constraint terms to obtain the joint dictionary more discriminatively. When the method is applied to the single-channel speech enhancement, speech components of the noisy signal can be more projected onto the clean speech sub-dictionary of the joint dictionary without being affected by the noise sub-dictionary, which makes the quality and intelligibility of the enhanced speech higher. The experimental results verify that our algorithm has better performance than the speech enhancement algorithm based on discriminative dictionary learning under white noise and colored noise environments in time domain waveform, spectrogram, global signal-to-noise ratio, subjective evaluation of speech quality, and logarithmic spectrum distance.

Learn structured analysis discriminative dictionary for multi-label classification

Learn structured analysis discriminative dictionary for multi-label classification

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.

Bats: An Appliance Safety Hazards Factors Detection Algorithm with an Improved Nonintrusive Load Disaggregation Method

In an electrical safe microenvironment, all kinds of electrical appliances can be operated safely to ensure the safety of life and property. The significance of safety hazard factors detection is to detect safety hazards in advance, to remind the administrators to exclude risk, to reduce the unnecessary loss, and to ensure that the electrical operation is healthy and orderly before the occurrence of accidents. In this paper, batteries are selected as the primary research subject of safety detection because batteries are used more and more in the Internet of Things (IOT), and they often cause fire in the process of discharging and charging. The existing algorithms need to be embedded into the specialized sensor for each important electrical appliance. However, they are limited by the actual deployment, so it is extremely difficult to spread widely. According to the opinions above, an improved load disaggregation algorithm based on dictionary learning and sparse coding with optimal dictionary matrix period is proposed to detect potential safety hazards of battery loads. For safety-related electrical applications, doing so can increase interpretability. Through experiments, we test this algorithm on the REDD dataset, and compare it with the baseline algorithms (combinatorial optimization, factorial hidden Markov model, basic discriminative dictionary sparse coding algorithm) to achieve a degree of trust. The Mean Absolute Error (MAE) value is 8.26, which drops by 70%. The Root Mean Square Error (RMSE) value is 97.75, which is also better than those baseline algorithms.

Stacking learning with coalesced cost filtering for accurate stereo matching

Deep learning based stereo matching algorithms have produced impressive disparity estimation for recent years; and the success of them has once overshadowed the conventional ones. In this paper, we intend to reverse this inferiority, by leveraging Stacking Learning with Coalesced Cost Filtering to make the conventional algorithms achieve or even surpass the results of deep learning ones. Four classical and Discriminative Dictionary Learning (DDL) algorithms are adopted as base-models for Stacking. For the former ones, four classical stereo matching algorithms are employed and regarded as ‘Coalesced Cost Filtering Module’; for the latter supervised learning one, we utilize the Discriminative Dictionary Learning (DDL) stereo matching algorithm. Then three categories of features are extracted from the predictions of base-models to train the meta-model. For the meta-model (final classifier) of Stacking, the Random Forest (RF) classifier is selected. In addition, we also employ an advanced one-view disparity refinement strategy to compute the final refined results more efficiently. Performance evaluations on Middlebury v.2 and v.3 stereo data sets demonstrate that the proposed algorithm outperforms other four most challenging stereo matching algorithms. Besides, the submitted online results even show better results than deep learning ones.

Multi-task analysis discriminative dictionary learning for one-class learning

One-class classification is a generalization of supervised learning based on one class of examples. It attracts growing attention in machine learning and data mining. In this paper, we propose a novel approach called multi-task dictionary learning for one-class learning (MTD-OC), which incorporates analysis discriminative dictionary learning into one-class learning. The analysis discriminative dictionary learning makes sure that dictionaries responding to different tasks are independent and discriminating as much as possible. The analysis discriminative dictionary learning simultaneously minimize l2,1-norm constraint, analysis incoherence term and sparse code extraction term, which aim to promote analysis incoherence and improve coding efficiency and accuracy for classification. The one-class classifier on the target task is then constructed by learning transfer knowledge from multiple source tasks. Here, one-class classification improves the performance of analysis discriminative dictionary, while analysis discriminative dictionary improves the performance of one-class classification term. In MTD-OC, the optimization function is formulated to deal with one-class classifier and analysis discriminative dictionary learning based on one class of examples. Then, we propose an iterative framework to solve the optimization function, and obtain the predictive classifier for the target class. Extensive experiments have shown that MTD-OC can improve the accuracy of one-class classifier by learning analysis discriminative dictionary from each task to construct a transfer classifier.

A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left Ventricular Detection.

Cardiac MRI left ventricular (LV) detection is frequently employed to assist cardiac registration or segmentation in computer-aided diagnosis of heart diseases. Focusing on the challenging problems in LV detection, such as the large span and varying size of LV areas in MRI, as well as the heterogeneous myocardial and blood pool parts in LV areas, a convolutional neural network (CNN) detection method combining discriminative dictionary learning and sequence tracking is proposed in this paper. To efficiently represent the different sub-objects in LV area, the method deploys discriminant dictionary to classify the superpixel oversegmented regions, then the target LV region is constructed by label merging and multi-scale adaptive anchors are generated in the target region for handling the varying sizes. Combining with non-differential anchors in regional proposal network, the left ventricle object is localized by the CNN based regression and classification strategy. In order to solve the problem of slow classification speed of discriminative dictionary, a fast generation module of left ventricular scale adaptive anchors based on sequence tracking is also proposed on the same individual. The method and its variants were tested on the heart atlas data set. Experimental results verified the effectiveness of the proposed method and according to some evaluation indicators, it obtained 92.95% in AP50 metric and it was the most competitive result compared to typical related methods. The combination of discriminative dictionary learning and scale adaptive anchor improves adaptability of the proposed algorithm to the varying left ventricular areas. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.