Essential Tensor Learning for Multi-View Spectral Clustering

By Jianlong Wu, Zhouchen Lin, Hongyuan Zha. IEEE Transactions on Image Processing, 2018

322 citations recorded by the source corpus.

Recently, multi-view clustering attracts much attention, which aims to take advantage of multi-view information to improve the performance of clustering. However, most recent work mainly focuses on the 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 the 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-of-the-art methods.

Subfields: Computational Mathematics, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b6b9faa618b4a926b5f3fd0cedef816044f286a9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mathematics

1. Hongyuan Zha
2. Fei Wang
3. Jianlong Wu
4. Zhouchen Lin

In Computational Mechanics

1. Hongyuan Zha
2. Jianlong Wu
3. Jungang Yang
4. Fei Wang
5. Chen Qian
6. Yang Sun
7. Zhouchen Lin
8. Wei An

In Computer Vision and Pattern Recognition

1. Jianlong Wu
2. Chen Qian
3. Fei Wang
4. Zhouchen Lin
5. Hongyuan Zha
6. Jungang Yang
7. Keyu Long
8. Cheng Li

Citations in the corpus

• Deep Comprehensive Correlation Mining for Image Clustering (2019, methodology): “Traditional clustering methods [55, 19, 6], such as K-means, spectral clustering [35, 48], and subspace clustering [31, 17] may fail for two main issues: first, hand-crafted features have limited capacity and cannot dynamically adjust to capture the prior distribution, especially when dealing with l”
• Infrared Dim and Small Target Detection via Multiple Subspace Learning and Spatial-Temporal Patch-Tensor Model (2021, background): “Recently, many tensor analysis based works have been applied in many applications, such as multiview clustering [45], subspace clustering [46], generating super resolution images [47], image processing, and video processing [48].”