Multiple-View Spectral Embedded Clustering Using a Co-training Approach

  title={Multiple-View Spectral Embedded Clustering Using a Co-training Approach},
  author={Hong Tao and Chenping Hou and Dong-yun Yi},
It is a challenging task to integrate multi-view representations, each of which is of high dimension to improve the clustering performance. In this paper, we aim to improve the clustering performance of spectral clustering method when the manifold for high-dimensional data is not well defined in the multiple-view setting. We abstract the discriminative information on each view by spectral embedded clustering which performs well on high-dimensional data without a clear low-dimensional manifold… 
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A Co-training Approach for Multi-view Spectral Clustering
A spectral clustering algorithm for the multi-view setting where the authors have access to multiple views of the data, each of which can be independently used for clustering, which has a flavor of co-training.
Multi-view clustering via canonical correlation analysis
Under the assumption that the views are un-correlated given the cluster label, it is shown that the separation conditions required for the algorithm to be successful are significantly weaker than prior results in the literature.
Multiple View Clustering Using a Weighted Combination of Exemplar-Based Mixture Models
A multiview clustering algorithm that associates a weight with each view and learns these weights automatically, and is computationally efficient and easy to implement, involving simple iterative computations.
Spectral Clustering with Two Views
The spectral clustering algorithm creates a bipartite graph and is based on the “minimizing-disagreement” idea and it is shown that it performs better than clustering in the joint space and clustered in the individual spaces when some patterns have both views and others have just one view.
Spectral Embedded Clustering
A new spectral clustering method, referred to as Spectral Embedded Clustering (SEC), to minimize the normalized cut criterion in spectral clusters as well as control the mismatch between the cluster assignment matrix and the low dimensional embedded representation of the data.
Spectral clustering and transductive learning with multiple views
This work develops multiview spectral clustering via generalizing the normalized cut from a single view to multiple views and further builds multivView transductive inference on the basis of multiv View spectral clusters.
A General Model for Multiple View Unsupervised Learning
The proposed model introduces the concept of mapping function to make the different patterns from different pattern spaces comparable and hence an optimal pattern can be learned from the multiple patterns of multiple representations.
Multiview Partitioning via Tensor Methods
This paper presents a novel tensor-based framework for integrating heterogeneous multiview data in the context of spectral clustering based on the integration of the Frobenius-norm objective function and shows that the solutions for both formulations can be computed by tensor decompositions.
A tutorial on spectral clustering
This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches.
Multiclass spectral clustering
  • Stella X. Yu, Jianbo Shi
  • Computer Science
    Proceedings Ninth IEEE International Conference on Computer Vision
  • 2003
This work proposes a principled account on multiclass spectral clustering by solving a relaxed continuous optimization problem by eigen-decomposition and clarifying the role of eigenvectors as a generator of all optimal solutions through orthonormal transforms.