Supervised dictionary learning with multiple classifier integration

Quan, Yuhui; Xu, Yong; Sun, Yeping; Huang, Yan

doi:10.1016/j.patcog.2016.01.028

Cited by 36 publications

(10 citation statements)

References 61 publications

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“…The classification accuracies of all methods are summarized in Table 3. Besides the algorithms already mentioned in the above experiments, we also add five state-of-the-art algorithms for comparison, which are EasyDL [45], MCDL [46], IC-DDL [47], SADL [48] and K-LSDSR [49]. We find that the analysis kernel KSVD algorithm can achieve the 96.8% classification accuracy, which shows that our method is competitive among all compared methods.…”

Section: E-yaleb Datasetmentioning

confidence: 89%

Nonlinear Kernel Dictionary Learning Algorithm Based on Analysis Sparse Model

Miao

Zhang

2020

IEEE Access

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Section: E-yaleb Datasetmentioning

confidence: 89%

Nonlinear Kernel Dictionary Learning Algorithm Based on Analysis Sparse Model

Miao

Zhang

2020

IEEE Access

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“…Typical discriminative sparse coding algorithms such as [4,5,9] focus on optimization problems generally similar to that of [4] as…”

Section: Discriminative Sparse Codingmentioning

confidence: 99%

“…In order to preserve the consistency between the test and training model, we also add the term β UΓ 2 F to the discriminant loss F of (6) which results in (9). Doing so, we want to make sure the trained dictionary U has a proper structure also regarding (5).…”

Section: Discriminative Recall Term G(l γ U)mentioning

confidence: 99%

Confident Kernel Sparse Coding and Dictionary Learning

Hosseini

Hammer

2018

2018 IEEE International Conference on Data Mining (ICDM)

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In recent years, kernel-based sparse coding (K-SRC) has received particular attention due to its efficient representation of nonlinear data structures in the feature space. Nevertheless, the existing K-SRC methods suffer from the lack of consistency between their training and test optimization frameworks. In this work, we propose a novel confident K-SRC and dictionary learning algorithm (CKSC) which focuses on the discriminative reconstruction of the data based on its representation in the kernel space. CKSC focuses on reconstructing each data sample via weighted contributions which are confident in its corresponding class of data. We employ novel discriminative terms to apply this scheme to both training and test frameworks in our algorithm. This specif design increases the consistency of these optimization frameworks and improves the discriminative performance in the recall phase. In addition, CKSC directly employs the supervised information in its dictionary learning framework to enhance the discriminative structure of the dictionary. For empirical evaluations, we implement our CKSC algorithm on multivariate time-series benchmarks such as DynTex++ and UTKinect. Our claims regarding the superior performance of the proposed algorithm are justified throughout comparing its classification results to the state-of-the-art K-SRC algorithms.

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“…An equiangular kernel dictionary is proposed in [29] to exploit the sparsity of high-dimensional visual data. In [30], [31], a dictionary and multiple classifiers are jointly learned to enhance the discriminative ability of the sparse codes. To capture the non-linear properties of data efficiently, a dictionary and a decision tree classifier are learned in [32].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Optimized Structured Dictionary for Discriminative Block Sparse Representation

Kumar

Sinha

2020

IEEE Access

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This paper presents a few innovations towards learning a discriminative block-structured dictionary. The learning process of such a dictionary is broadly divided into two steps: block formation and dictionary update. In the existing works on block structure estimation, it is assumed that the maximum block size is known a priori. In real-world problems, such an assumption may be sub-optimal. For addressing that, a genetic algorithm optimized K-means clustering based block formation approach is proposed in this work. We also propose a novel dictionary learning approach that incorporates three attributes, namely, reconstruction and discriminative fidelities, block-wise incoherence, and 2,1-norm regularization. To further enhance the discriminative ability of the sparse codes, the class-specific and class-common information are modeled separately in the dictionary. The 2,1-norm regularization enhances the consistency among sparse codes belonging to the same-class data. In the proposed approach, the dictionary is updated block-wise by employing the singular value decomposition of the composite error matrix obtained through the weighted combination of the component errors. The proposed innovations are evaluated on several public image databases for super-resolution and classification tasks. Along with those image databases, speech based speaker verification task is also evaluated the proposed approach in a few different domains to validate the generalizability. The experimental results obtained on these different databases demonstrate the effectiveness of the proposed approaches when compared with the respective state-of-the-art.

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Supervised dictionary learning with multiple classifier integration

Cited by 36 publications

References 61 publications

Nonlinear Kernel Dictionary Learning Algorithm Based on Analysis Sparse Model

Nonlinear Kernel Dictionary Learning Algorithm Based on Analysis Sparse Model

Confident Kernel Sparse Coding and Dictionary Learning

Genetic Algorithm Optimized Structured Dictionary for Discriminative Block Sparse Representation

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