Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering

Li, Xuelong; Cui, Guosheng; Dong, Yongsheng

doi:10.1109/tcyb.2016.2585355

Cited by 168 publications

(59 citation statements)

References 55 publications

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“…To tackle the limitations of the pairwise relation based methods, the hyper-graph relation [63], [64], [65] is proposed and the related literature follows two different directions. Some transform the hyper-correlation into a simpler pairwise graph [21], [66], followed by a standard graph clustering method, e.g., normalized cut [11], to calculate the assignments.…”

Section: Hyper-graph Clusteringmentioning

confidence: 99%

Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship

Liang

Yang

Cheng

et al. 2019

IEEE Trans. on Image Process.

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In this paper we propose a unified framework to simultaneously discover the number of clusters and group the data points into them using subspace clustering. Real data distributed in a high-dimensional space can be disentangled into a union of low-dimensional subspaces, which can benefit various applications. To explore such intrinsic structure, stateof-the-art subspace clustering approaches often optimize a selfrepresentation problem among all samples, to construct a pairwise affinity graph for spectral clustering. However, a graph with pairwise similarities lacks robustness for segmentation, especially for samples which lie on the intersection of two subspaces. To address this problem, we design a hyper-correlation based data structure termed as the triplet relationship, which reveals high relevance and local compactness among three samples. The triplet relationship can be derived from the self-representation matrix, and be utilized to iteratively assign the data points to clusters. Three samples in each triplet are encouraged to be highly correlated and are considered as a meta-element during clustering, which show more robustness than pairwise relationships when segmenting two densely distributed subspaces. Based on the triplet relationship, we propose a unified optimizing scheme to automatically calculate clustering assignments. Specifically, we optimize a model selection reward and a fusion reward by simultaneously maximizing the similarity of triplets from different clusters while minimizing the correlation of triplets from same cluster. The proposed algorithm also automatically reveals the number of clusters and fuses groups to avoid oversegmentation. Extensive experimental results on both synthetic and real-world datasets validate the effectiveness and robustness of the proposed method.

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Section: Hyper-graph Clusteringmentioning

confidence: 99%

Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship

Liang

Yang

Cheng

et al. 2019

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…In the Euclidean space, the standard nonnegative matrix factorization in Eq. (2) fails to discover the intrinsic geometrical and discriminating structure of the data space [65,66].…”

Section: The Model Of Gnmflmimentioning

confidence: 99%

“…{0.0001,0.001,0.01} for and the ranges of = ∈ {0.001,0.01,0.1} . Based on the studies of Cai et al [46] and Li et al [65], we set = 5. Finally, the parameter values are = 5, k=80, = 0.01, = = 0.1.…”

Section: Experimental Settingsmentioning

confidence: 99%

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

Wang

You

et al. 2019

Preprint

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Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been involved in various biological processes. Emerging evidence suggests that the interactions between lncRNAs and miRNAs play an important role in regulating of genes and the development of many diseases. Due to the limited scale of known lncRNA-miRNA interactions, and expensive time and labor costs for identifying them by biological experiments, more accurate and efficient lncRNA-miRNA interactions computational prediction approach urgently need to be developed. In this work, we proposed a novel computational method, GNMFLMI, to predict lncRNA-miRNA interactions using graph regularized nonnegative matrix factorization. More specifically, the similarities both lncRNA and miRNA are calculated based on known interaction information and their sequence information. Then, the affinity graphs for lncRNAs and miRNAs are constructed using the p-nearest neighbors, respectively. Finally, a graph regularized nonnegative matrix factorization model is developed to accurately identify potential interactions between lncRNAs and miRNAs. To evaluate the performance of GNMFLMI, five-fold cross validation experiments are carried out. GNMFLMI achieves the AUC value of 0.9769 which outperforms the compared methods NMF and CNMF. In the case studies for lncRNA nonhsat159254.1 and miRNA hsa-mir-544a, 20 and 16 of the top-20 associations predicted by GNMFLMI are confirmed, respectively. Rigorous experimental results demonstrate that GNMFLMI can effectively predict novel lncRNA-miRNA interactions, which can provide guidance for relevant biomedical research.

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“…Therefore, its resulting sparse codes are more suitable for the clustering purpose. In addition, our post-processing technique can contribute to non-negative SSC methods such as [18,14,15] to improve their latent representations.…”

Section: Related Workmentioning

confidence: 99%

Non-negative Local Sparse Coding for Subspace Clustering

Hosseini

Hammer

2018

Advances in Intelligent Data Analysis XVII

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Subspace sparse coding (SSC) algorithms have proven to be beneficial to clustering problems. They provide an alternative data representation in which the underlying structure of the clusters can be better captured. However, most of the research in this area is mainly focused on enhancing the sparse coding part of the problem. In contrast, we introduce a novel objective term in our proposed SSC framework which focuses on the separability of data points in the coding space. We also provide mathematical insights into how this local-separability term improves the clustering result of the SSC framework. Our proposed non-linear local SSC algorithm (NLSSC) also benefits from the efficient choice of its sparsity terms and constraints. The NLSSC algorithm is also formulated in the kernel-based framework (NLKSSC) which can represent the nonlinear structure of data. In addition, we address the possibility of having redundancies in sparse coding results and its negative effect on graph-based clustering problems. We introduce the link-restore post-processing step to improve the representation graph of non-negative SSC algorithms such as ours. Empirical evaluations on well-known clustering benchmarks show that our proposed NLSSC framework results in better clusterings compared to the stateof-the-art baselines and demonstrate the effectiveness of the link-restore post-processing in improving the clustering accuracy via correcting the broken links of the representation graph.

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Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering

Cited by 168 publications

References 55 publications

Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship

Simultaneous Subspace Clustering and Cluster Number Estimating Based on Triplet Relationship

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

Non-negative Local Sparse Coding for Subspace Clustering

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