Multi-hypergraph Incidence Consistent Sparse Coding for Image Data Clustering

Feng, Xiaodong; Wu, Sen; Zhou, Wenjun; Tang, Zhiwei

doi:10.1007/978-3-319-31750-2_7

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…On the other hand, the two-step scheme makes GSC be lost in a local optima. Many improved approaches have been provided [21], [23], [24], but the predicament is still not broken.…”

Section: B Graph Laplacian Regularizationmentioning

confidence: 99%

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Integrated Sparse Coding With Graph Learning for Robust Data Representation

Zhang

Liu

2020

IEEE Access

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Sparse coding is a popular technique for achieving compact data representation and has been used in many applications. However, the instability issue often causes degeneration in practice and thus attracts a lot of studies. While the traditional graph sparse coding preserves the neighborhood structure of the data, this study integrates the low-rank representation(LRR) to fix the inconsistency of sparse coding by holding the subspace structures of the high-dimensional observations. The proposed method is dubbed low-rank graph regularized sparse coding (LogSC), which learns sparse codes and low-rank representations jointly rather than the traditional two-step approach. Since the two data representations share a dictionary matrix, the resulted sparse representation on this dictionary could be benefited from LRR. We solved the optimization problem of LogSC by using the linearized alternating direction method with adaptive penalty. Experimental results show the proposed method is discriminative in feature learning and robust to various noises. This work provides a one-step approach to integrating graph embedding in representation learning. INDEX TERMS Graph sparse coding, graph embedding, data structure preserving, robust data representation, image representation, one-step integrated graph sparse coding.

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“…On the other hand, the two-step scheme makes GSC be lost in a local optima. Many improved approaches have been provided [21], [23], [24], but the predicament is still not broken.…”

Section: B Graph Laplacian Regularizationmentioning

confidence: 99%

“…In [21], ensemble manifold regularization [22] integrates multiple Face images TSC GSC LogSC graphs to avoid hype-parameter selections. In [23], a hypergraph incidence consistency term was introduced into multihypergraph sparse coding. For similar images, GSC produces two slightly similar sparse codes, as shown in FIGURE 1.…”

Section: Introductionmentioning

confidence: 99%

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Zhang

Liu

2020

IEEE Access

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Low-Rank Graph Regularized Sparse Coding

Zhang

Liu

Shang

et al. 2018

Lecture Notes in Computer Science

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Multi-Hypergraph Consistent Sparse Coding

Feng

Zhou

2017

ACM Trans. Intell. Syst. Technol.

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Sparse representation has been a powerful technique for modeling high-dimensional data. As an unsupervised technique to extract sparse representations, sparse coding encodes the original data into a new sparse code space and simultaneously learns a dictionary representing high-level semantics. Existing methods have considered local manifold within high-dimensional data using graph/hypergraph Laplacian regularization, and more from the manifold could be utilized to improve the performance. In this article, we propose to further regulate the sparse coding so that the learned sparse codes can well reconstruct the hypergraph structure. In particular, we add a novel hypergraph consistency regularization term (HC) by minimizing the reconstruction error of the hypergraph incidence or weight matrix. Moreover, we extend the HC term to multi-hypergraph consistent sparse coding (MultiCSC) and automatically select the optimal manifold structure under the multi-hypergraph learning framework. We show that the optimization of MultiCSC can be solved efficiently, and that several existing sparse coding methods can fit into the general framework of MultiCSC as special cases. As a case study, hypergraph incidence consistent sparse coding is applied to perform semi-auto image tagging, demonstrating the effectiveness of hypergraph consistency regulation. We perform further experiments using MultiCSC for image clustering, which outperforms a number of baselines.

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Multi-hypergraph Incidence Consistent Sparse Coding for Image Data Clustering

Cited by 3 publications

References 14 publications

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Low-Rank Graph Regularized Sparse Coding

Multi-Hypergraph Consistent Sparse Coding

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