Discriminative dictionary learning via Fisher discrimination K-SVD algorithm

Zheng, Hao; Tao, Dapeng

doi:10.1016/j.neucom.2015.03.071

Cited by 48 publications

(15 citation statements)

References 29 publications

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“…DL on the other hand is to learn a good dictionary from training samples so that a given signal could be well represented; hence the quality of the dictionary is very crucial for efficient SR [4]. The dictionary could be determined by either using all the training samples as the dictionary to code the test samples (e.g., locality-constrained linear coding (LLC) in [5]) or adopting a learned dictionary for the sparse representation for each training sample in the set (e.g., KSVD in [6], Fisher discriminative dictionary learning (FDDL) [7]). Besides, group-centered sparse coding likened to rank minimization problem is used to measure the sparse coefficient of each group by estimating the values of each grouping in [8].…”

Section: Introductionmentioning

confidence: 99%

Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Benuwa

Zhan

Ghansah

et al. 2018

Mathematical Problems in Engineering

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Dictionary learning (DL) and sparse representation (SR) based classifiers have greatly impacted the classification performance and have had good recognition rate on image data. In video semantic analysis (VSA), the local structure of video data contains more vital discriminative information needed for classification. However, this has not been fully exploited by the current DL based approaches. Besides, similar coding findings are not being realized from video features with the same video category. Based on the issues stated afore, a novel learning algorithm, called sparsity based locality-sensitive discriminative dictionary learning (SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of locality-sensitive dictionary learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experiment results show that the proposed SLSDDL significantly improves the performance of video semantic detection compared with the comparative state-of-the-art approaches. Moreover, the robustness to various diverse environments in video is also demonstrated, which proves the universality of the novel approach.

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Section: Introductionmentioning

confidence: 99%

Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Benuwa

Zhan

Ghansah

et al. 2018

Mathematical Problems in Engineering

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show abstract

“…The bandelets introduced by Mallat et al were spatially adapted to better treat images by orienting the regular wavelet atoms along the principal direction of the local area processed [43]. The Method of Optimal Directions (MOD) and K-singular value decomposition (K-SVD) [44,45] are popular dictionary learning algorithms. Fisher discriminative dictionary, using three dictionary training mechanisms, has attracted considerable attention.…”

Section: Sparse Dictionary Constructed Based On Fisher Discriminativementioning

confidence: 99%

Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System

Tang

Chai

et al. 2018

Applied Sciences

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Fault detection and diagnosis in the chemical industry is a challenging task due to the large number of measured variables and complex interactions among them. To solve this problem, a new fault diagnosis method named Fisher discriminative sparse representation (FDSR), based on deep belief network (DBN), is proposed in this paper. We used DBN to extract the features of all faulty and normal modes. Features extracted by the DBN were used to learn subdictionaries, then the overcomplete dictionary was constructed by cascading all subdictionaries in order, and each dictionary atom corresponded to class labels. The Fisher discrimination criterion (FDC) was applied to the dictionary learning to ensure smaller within-class scatter but greater between-class scatter. The quadratic programming method was applied to estimate the sparse coefficients simultaneously class by class. Therefore, both the reconstruction error and sparse coefficients were discriminative, so that the reconstruction error after sparse coding can be used for pattern classification. An experiment performed on the Tennessee Eastman (TE) process indicated that compared with the traditional monitoring methods, the FDSR based on DBN produced more interpretable results and achieved superior performance for feature extraction and classification in the field of complex system fault diagnosis. T-distributed stochastic neighbor embedding (t-SNE) appropriately visualized the performance of the proposed method and produced reliable fault diagnosis results.

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“…In the coding phase, training samples are combined together as a dictionary. In recent years, lots of methods for constructing dictionary have appeared, such as FDDL [18], JDDLDR [20] and DKSVD [21] etc. These methods have one thing in common: the dictionary is constructed by a learning algorithm to improve its discrimination ability as much as possible.…”

Section: Transform Domain Sparse Representation-based Classification mentioning

confidence: 99%

Transform-domain sparse representation based classification for machinery vibration signals

Yu¹,

Fan²,

Wang³

et al. 2018

J. vibroeng.

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The working state of machinery can be reflected by vibration signals. Accurate classification of these vibration signals is helpful for the machinery fault diagnosis. A novel classification method for vibration signals, named Transform Domain Sparse Representation-based Classification (TDSRC), is proposed. The method achieves high classification accuracy by three steps. Firstly, time-domain vibration signals, including training samples and test samples, are transformed to another domain, e.g. frequency-domain, wavelet-domain etc. Then, the transform coefficients of the training samples are combined as a dictionary and the transform coefficients of the test samples are sparsely coded on the dictionary. Finally, the class label of the test samples is identified by their minimal reconstruction errors. Although the proposed method is very similar to the Sparse Representation-based Classification (SRC), experimental results illustrates its performance is far superior to SRC in the classification of vibration signals. These experiments include: frequency-domain classification of bearing vibration data from the Case Western Reserve University (CWRU) Bearing Data Center and wavelet-domain classification of six fault-types gearbox vibration data from our rotating machinery experimental platform.

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Discriminative dictionary learning via Fisher discrimination K-SVD algorithm

Cited by 48 publications

References 29 publications

Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System

Transform-domain sparse representation based classification for machinery vibration signals

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