Multiple graph regularized sparse coding and multiple hypergraph regularized sparse coding for image representation

Jin, Taisong; Yu, Zhengtao; Li, Cuihua

doi:10.1016/j.neucom.2014.11.067

Cited by 28 publications

(19 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the first step, Euclidean distance is often used to compute the pairwise similarity. The second step uses the feature-sign search algorithm [25] for sparse decomposition and the Lagrange dual algorithm [5], [8], [9], [21] for dictionary learning. However, this avenue of GSC has the following drawbacks.…”

Section: A Our Motivationsmentioning

confidence: 99%

“…Hence, a hypergraph could capture high-order relationships among the samples and has been developed for image classification [19] and dimensionality reduction [20]. 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.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Zhang

Liu

2020

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: A Our Motivationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Zhang

Liu

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…. Using a similar approach where we substitute C with GZ T , and then replace G with the right hand side of (13) in (7) we can find the optimal selection for step-size c G as c κ+1…”

Section: Gradient Descent Based Algorithmmentioning

confidence: 99%

Graph Filtering For Data Reduction and Reconstruction

Schizas

2019

2019 Data Compression Conference (DCC)

View full text Add to dashboard Cite

A novel approach is put forth that utilizes data similarity, quantified on a graph, to improve upon the reconstruction performance of principal component analysis. The tasks of data dimensionality reduction and reconstruction are formulated as graph filtering operations, that enable the exploitation of data node connectivity in a graph via the adjacency matrix. The unknown reducing and reconstruction filters are determined by optimizing a mean-square error cost that entails the data, as well as their graph adjacency matrix. Working in the graph spectral domain enables the derivation of simple gradient descent recursions used to update the matrix filter taps. Numerical tests in real image datasets demonstrate the better reconstruction performance of the novel method over standard principal component analysis.

show abstract

“…Many research efforts tried to use sparse representation in visual content analysis and promising results are reported [25], [32], [54], [60]. For example, in [60], Yuan et al addressed the problem of visual classification with multiple features and proposed a multitask joint sparse representation model to combine the strengths of multiple features for recognition.…”

Section: Related Workmentioning

confidence: 99%

“…Motivated by the fact that kernel trick can capture the nonlinear similarity of features, Gao et al [15] proposed a kernel sparse representation method to represent high-dimensional features by mapping them with an implicit kernel function. In [25], to learn both optimal intrinsic manifold and sparse code jointly, Jin et al represented images by sparse coding with a graph regularizer. In [14], to address the problem of automatically uncovering the underlying group structure from images, Feng et al proposed a novel auto-grouped sparse representation method which could group semantically correlated feature elements together by optimally fusing their multiple sparse representations.…”

Section: Related Workmentioning

confidence: 99%

Visual Classification by <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="wang-ieq1-2415497.gif"/></alternatives></inline-formula>-Hypergraph Modeling

Wang

Liu

2015

IEEE Trans. Knowl. Data Eng.

143

View full text Add to dashboard Cite

Visual classification has attracted considerable research interests in the past decades. In this paper, a novel ' 1 -hypergraph model for visual classification is proposed. Hypergraph learning, as a natural extension of graph model, has been widely used in many machine learning tasks. In previous work, hypergraph is usually constructed by attribute-based or neighborhood-based methods. That is, a hyperedge is generated by connecting a set of samples sharing a same feature attribute or in a neighborhood. However, these methods are unable to explore feature space globally or sensitive to noises. To address these problems, we propose a novel hypergraph construction approach that leverages sparse representation to generate hyperedges and learns the relationship among hyperedges and their vertices. First, for each sample, a hyperedge is generated by regarding it as the centroid and linking it as well as its nearest neighbors. Then, the sparse representation method is applied to represent the centroid vertex by other vertices within the same hyperedge. The vertices with zero coefficients are removed from the hyperedge. Finally, the representation coefficients are used to define the incidence relation between the hyperedge and the vertices. In our approach, we also optimize the hyperedge weights to modulate the effects of different hyperedges. We leverage the prior knowledge on the hyperedges so that the hyperedges sharing more vertices can have closer weights, where a graph Laplacian is used to regularize the optimization of the weights. Our approach is named ' 1 -hypergraph since the ' 1 sparse representation is employed in the hypergraph construction process. The method is evaluated on various visual classification tasks, and it demonstrates promising performance.

show abstract

Multiple graph regularized sparse coding and multiple hypergraph regularized sparse coding for image representation

Cited by 28 publications

References 38 publications

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Graph Filtering For Data Reduction and Reconstruction

Visual Classification by <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="wang-ieq1-2415497.gif"/></alternatives></inline-formula>-Hypergraph Modeling

Contact Info

Product

Resources

About