Discriminative ICA model with reconstruction constraint for image classification

Xiao, Yanhui; Zhu, Zhongkui; Wei, Shikui; Zhao, Yao

doi:10.1145/2393347.2396349

Cited by 2 publications

(10 citation statements)

References 7 publications

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“…Following the common experiment setup [16], we implement our algorithm on 15 and 30 training images per category with basis size K = 1020 and 10 × 10 receptive fields, respectively. Table I with ScSPM [16], D-KSVD [4], LC-KSVD [18], RICA [9], KICA [21], KSR [26], and DRICA [12].…”

Section: Methodsmentioning

confidence: 99%

“…To achieve this goal, an inhomogeneous representation energy constraint [12] was introduced as follows:…”

Section: A Class-driven Discrimination Constraintmentioning

confidence: 99%

“…Given a test sample, (12) means that the learned basis set can sparsely represent it while demanding its homogeneous representations to be as large as possible and its inhomogeneous representations to be as small as possible. Following kRICA, the optimization problem (12) can be easily solved by the proposed fixed point algorithm.…”

Section: A Class-driven Discrimination Constraintmentioning

confidence: 99%

“…This paper is fundamentally based on our previous work, Discriminative Reconstruction Independent Component Analysis (DRICA) [12], which only minimized inhomogeneous representation energy to learn a discriminative sparse representation. DRICA failed to explicitly control homogeneous energy; thus, the resulting learned sparse representation may lack discrimination ability.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Kernel Reconstruction ICA for Sparse Representation

Xiao

Zhu

Zhao

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

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Independent component analysis with soft reconstruction cost (RICA) has been recently proposed to linearly learn sparse representation with an overcomplete basis, and this technique exhibits promising performance even on unwhitened data. However, linear RICA may not be effective for the majority of real-world data because nonlinearly separable data structure pervasively exists in original data space. Meanwhile, RICA is essentially an unsupervised method and does not employ class information. Motivated by the success of the kernel trick that maps a nonlinearly separable data structure into a linearly separable case in a high-dimensional feature space, we propose a kernel RICA (kRICA) model to nonlinearly capture sparse representation in feature space. Furthermore, we extend the unsupervised kRICA to a supervised one by introducing a class-driven discrimination constraint, such that the data samples from the same class are well represented on the basis of the corresponding subset of basis vectors. This discrimination constraint minimizes inhomogeneous representation energy and maximizes homogeneous representation energy simultaneously, which is essentially equivalent to maximizing between-class scatter and minimizing within-class scatter at the same time in an implicit manner. Experimental results demonstrate that the proposed algorithm is more effective than other state-of-the-art methods on several datasets.

show abstract

Section: Methodsmentioning

confidence: 99%

“…To achieve this goal, an inhomogeneous representation energy constraint [12] was introduced as follows:…”

Section: A Class-driven Discrimination Constraintmentioning

confidence: 99%

Section: A Class-driven Discrimination Constraintmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Kernel Reconstruction ICA for Sparse Representation

Xiao

Zhu

Zhao

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

show abstract

“…It is important to note that this work is fundamentally based on our previous work DRICA [15]. In comparison to DRICA, we further improve our work as follows:…”

Section: Introductionmentioning

confidence: 99%

Kernel Reconstruction ICA for Sparse Representation

Xiao¹,

Zhu²,

Zhao³

2013

Preprint

Self Cite

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Independent Component Analysis (ICA) is an effective unsupervised tool to learn statistically independent representation. However, ICA is not only sensitive to whitening but also difficult to learn an over-complete basis. Consequently, ICA with soft Reconstruction cost(RICA) was presented to learn sparse representations with over-complete basis even on unwhitened data. Whereas RICA is infeasible to represent the data with nonlinear structure due to its intrinsic linearity. In addition, RICA is essentially an unsupervised method and can not utilize the class information. In this paper, we propose a kernel ICA model with reconstruction constraint (kRICA) to capture the nonlinear features. To bring in the class information, we further extend the unsupervised kRICA to a supervised one by introducing a discrimination constraint, namely d-kRICA. This constraint leads to learn a structured basis consisted of basis vectors from different basis subsets corresponding to different class labels. Then each subset will sparsely represent well for its own class but not for the others. Furthermore, data samples belonging to the same class will have similar representations, and thereby the learned sparse representations can take more discriminative power. Experimental results validate the effectiveness of kRICA and d-kRICA for image classification.

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Discriminative ICA model with reconstruction constraint for image classification

Cited by 2 publications

References 7 publications

Kernel Reconstruction ICA for Sparse Representation

Kernel Reconstruction ICA for Sparse Representation

Kernel Reconstruction ICA for Sparse Representation

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