Multi-feature Spectral Clustering with Minimax Optimization

Wang, Hongxing; Weng, Chaoqun; Yuan, Junsong

doi:10.1109/cvpr.2014.523

Cited by 55 publications

(24 citation statements)

References 28 publications

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“…[4] aims to find the complementary information across views based on a co-regularization method. [22] tries to find a universal Laplacian embedding for multi-view features using minimax optimization. The work in [23], [24] shows that there is a natural connection between the spectral clustering and the Markov random walk.…”

Section: Related Workmentioning

confidence: 99%

Essential Tensor Learning for Multi-View Spectral Clustering

Lin

Zha

2019

IEEE Trans. on Image Process.

276

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Multi-view clustering attracts much attention recently, which aims to take advantage of multi-view information to improve the performance of clustering. However, most recent work mainly focus on self-representation based subspace clustering, which is of high computation complexity. In this paper, we focus on the Markov chain based spectral clustering method and propose a novel essential tensor learning method to explore the high order correlations for multi-view representation. We first construct a tensor based on multi-view transition probability matrices of the Markov chain. By incorporating the idea from robust principle component analysis, tensor singular value decomposition (t-SVD) based tensor nuclear norm is imposed to preserve the low-rank property of the essential tensor, which can well capture the principle information from multiple views. We also employ the tensor rotation operator for this task to better investigate the relationship among views as well as reduce the computation complexity. The proposed method can be efficiently optimized by the alternating direction method of multipliers (ADMM). Extensive experiments on seven real world datasets corresponding to five different applications show that our method achieves superior performance over other state-ofthe-art methods.

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Section: Related Workmentioning

confidence: 99%

Essential Tensor Learning for Multi-View Spectral Clustering

Lin

Zha

2019

IEEE Trans. on Image Process.

276

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“…x p i || 1 = 1. For MinimaxMVSC, as described in (Wang et al, 2014), the similarity matrix is constructed based on the Gaussian kernel in which Euclidean distance is used. Note that the above initialization method will generate the same set of initial centroids, hence the clustering results of each run is same for each approach.…”

Section: Results On Image Data Setsmentioning

confidence: 99%

“…MinimaxMVSC (Wang et al, 2014) is a multi-view spectral clustering approach based on minimax optimization. In MinimaxMVSC, the first strategy is used to formulate the objective function as follows:…”

Section: Minimaxmvscmentioning

confidence: 99%

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Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources

Wang

Chen

2017

Expert Systems with Applications

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Multi-view data clustering refers to categorizing a data set by making good use of related information from multiple representations of the data. It becomes important nowadays because more and more data can be collected in a variety of ways, in different settings and from different sources, so each data set can be represented by different sets of features to form different views of it. Many approaches have been proposed to improve clustering performance by exploring and integrating heterogeneous information underlying different views. In this paper, we propose a new multi-view fuzzy clustering approach called Mini-maxFCM by using minimax optimization based on well-known Fuzzy c means.In MinimaxFCM the consensus clustering results are generated based on minimax optimization in which the maximum disagreements of different weighted views are minimized. Moreover, the weight of each view can be learned automatically in the clustering process. In addition, there is only one parameter to be set besides the fuzzifier. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed MinimaxFCM are provided here. Experimental studies on nine multi-view data sets including real

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“…Based on this, this thesis will discuss our recent progress on multi-feature fusion using multiple feature clustered patterns [45,46]. Unlike much previous work on cooccurrence pattern discovery [32] in spatial domain, e.g., [35,36] and [37], it mainly aims to capture compositional patterns across multiple feature modalities, thus can naturally combine multiple features.…”

Section: Multi-feature Fusion For Visual Pattern Discoverymentioning

confidence: 99%

Discovering visual patterns for image data analysis

Wang¹

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Multi-feature Spectral Clustering with Minimax Optimization

Cited by 55 publications

References 28 publications

Essential Tensor Learning for Multi-View Spectral Clustering

Essential Tensor Learning for Multi-View Spectral Clustering

Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources

Discovering visual patterns for image data analysis

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