Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning

Zhang, Zhao; Ren, Jiahuan; Jiang, Weizhong; Zhang, Zheng; Hong, Richang; Yan, Shuicheng; Wang, Meng

doi:10.1109/tcsvt.2019.2923007

Cited by 33 publications

(18 citation statements)

References 42 publications

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“…classification error is not incorporated in the objective function). In several variants of discriminative DL methods are proposed to improve the data representation and classification abilities by encoding the locality and reconstruction error into the DL procedures, while some of them aim to concurrently improve the scalability of the algorithms by getting rid of costly norms [26,27,28]. Recently, DL has also been extended to deep learning frameworks [29], which seek multiple dictionaries at different image scales capturing also complementary coherent characteristics.…”

Section: Related Workmentioning

confidence: 99%

Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

et al. 2020

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The field of neuroscience is experiencing rapid growth in the complexity and quantity of the recorded neural activity allowing us unprecedented access to its dynamics in different brain areas. The objective of this work is to discover directly from the experimental data rich and comprehensible models for brain function that will be concurrently robust to noise. Considering this task from the perspective of dimensionality reduction, we develop an innovative, robust to noise dictionary learning framework based on adversarial training methods for the identification of patterns of synchronous firing activity as well as within a time lag. We employ real-world binary datasets describing the spontaneous neuronal activity of laboratory mice over time, and we aim to their efficient low-dimensional representation. The results on the classification accuracy for the discrimination between the clean and the adversarial-noisy activation patterns obtained by an SVM classifier highlight the efficacy of the proposed scheme compared to other methods, and the visualization of the dictionary's distribution demonstrates the multifarious information that we obtain from it.

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Section: Related Workmentioning

confidence: 99%

Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

et al. 2020

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“…ITH the increasing complexity of contents, diversity of distribution and high-dimensionality of real data, how to represent data efficiently for subsequent classification or clustering still remains an important research topic [1][2][3][9] [50]. To represent data, some feasible methods can be used, such as sparse representation (SR) by dictionary learning (DL) [4][5][6][7][8], low-rank coding [9][10][15] [38][39] and matrix factorization [11] [12], which are inspired by the fact that high-dimensional data can usually be characterized by applying a low-dimensional or compressed space in which the possible noise and redundant information can be removed in addition to preserving the useful information and important structures.…”

Section: Introductionmentioning

confidence: 99%

“…Although DPL and ADDL aim to address this issue by calculating a synthesis dictionary jointly, they both did not consider regularizing the synthesis dictionary to obtain the salient low-rank and sparse coefficients. Most real data can usually be represented using a sparse and/or low-rank subspace due to the intrinsic low-dimensional characteristics [4][5][6][7][8][9][10]. Thus, without considering the joint sparse and low-rank constraints properly, the resulted structures of the coefficients may not represent the given data appropriately and accurately.…”

Section: Introductionmentioning

confidence: 99%

Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space

Ren

Zhang

et al. 2020

IEEE Trans. on Image Process.

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In this paper, we investigate the robust dictionary learning (DL) to discover the hybrid salient low-rank and sparse representation in a factorized compressed space. A Joint Robust Factorization and Projective Dictionary Learning (J-RFDL) model is presented. The setting of J-RFDL aims at improving the data representations by enhancing the robustness to outliers and noise in data, encoding the reconstruction error more accurately and obtaining hybrid salient coefficients with accurate reconstruction ability. Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient. To make the encoding process robust to noise in data, J-RFDL clearly uses sparse L2, 1-norm that can potentially minimize the factorization and reconstruction errors jointly by forcing rows of the reconstruction errors to be zeros. To deliver salient coefficients with good structures to reconstruct given data well, J-RFDL imposes the joint low-rank and sparse constraints on the embedded coefficients with a synthesis dictionary. Based on the hybrid salient coefficients, we also extend J-RFDL for the joint classification and propose a discriminative J-RFDL model, which can improve the discriminating abilities of learnt coefficients by minimizing the classification error jointly. Extensive experiments on public datasets demonstrate that our formulations can deliver superior performance over other state-of-the-art methods. Index Terms-Hybrid salient representation, robust factorized compression, robust projective dictionary learning, classificationW ---------------- J. Ren is with the

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“…It is also worth noticing that the dictionary size, i.e., number of atoms, also has direct effect on the complexity of the compact representation of data. Thus, learning a good dictionary with the strong distinguishing power is crucial for the data representation and classification [1][2][3][4][5][6][7][8][9][10][11][12] [41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification

Zhang

Sun

Zhang

et al. 2019

2019 IEEE International Conference on Data Mining (ICDM)

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In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. As a result, TP-DPL unifies the salient feature extraction, representation and classification. The twin-incoherence constraint on codes and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve the local neighborhood of the coefficients and salient features within each class explicitly. For efficiency, TP-DPL uses Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance. Index Terms-Structured twin-incoherence, twin-projective latent dictionary pair learning, structured adaptive weighting, discriminative classification I.

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Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning

Cited by 33 publications

References 42 publications

Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space

Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification

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