Multi-View Constraint Propagation with Consensus Prior Knowledge

In many applications, the pairwise constraint is a kind of weaker supervisory information which can be collected easily. The constraint propagation has been proved to be a success of exploiting such side-information. In recent years, some methods of multi-view constraint propagation have been proposed. However, the problem of reasonably fusing different views remains unaddressed. In this paper, we present a method dubbed Consensus Prior Constraint Propagation (CPCP), which can provide the prior knowledge of the robustness of each data instance and its neighborhood. With the robustness generated from the consensus information of each view, we build a unified affinity matrix as a result of the propagation. Specifically, we fuse the affinity of different views at a data instance level instead of a view level. This paper also introduces an approach to deal with the imbalance between the positive and negative constraints. The proposed method has been tested in clustering tasks on two publicly available multi-view data sets to show the superior performance.

• Publication year

  2016

• Venue

  arXiv.org

• Publication date

  2016-09-21

• Fields of study

  Computer Science

• Identifiers
  arXiv 1609.06456
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Discriminatively Embedded K-Means for Multi-view Clustering

  2016cited by this paper
• Constructing Robust Affinity Graphs for Spectral Clustering

  2014cited by this paper
• Multi-View Clustering via Joint Nonnegative Matrix Factorization

  2013cited by this paper
• Consensus of k-NNs for Robust Neighborhood Selection on Graph-Based Manifolds

  2013cited by this paper
• Unified Constraint Propagation on Multi-View Data

  2013cited by this paper
• Metric Learning: A Survey

  2013cited by this paper
• Co-regularized Multi-view Spectral Clustering

  2011cited by this paper
• Symmetric Graph Regularized Constraint Propagation

  2011influential reference
• Exhaustive and Efficient Constraint Propagation: A Graph-Based Learning Approach and Its Applications

  2011cited by this paper
• Multi-modal constraint propagation for heterogeneous image clustering

  2011influential reference
• Multi-view clustering with constraint propagation for learning with an incomplete mapping between views

  2010cited by this paper
• Constrained Spectral Clustering via Exhaustive and Efficient Constraint Propagation

  2010cited by this paper
• TagProp: Discriminative metric learning in nearest neighbor models for image auto-annotation

  2009cited by this paper
• Unified Video Annotation via Multigraph Learning

  2009cited by this paper
• Constrained spectral clustering through affinity propagation

  2008cited by this paper
• Dominant Sets and Pairwise Clustering

  2007cited by this paper
• A tutorial on spectral clustering

  2007cited by this paper
• The Pascal Visual Object Classes Challenge 2006 ( VOC 2006 ) Results

  2006cited by this paper
• The PASCAL Visual Object Classes Challenge

  2006cited by this paper
• Learning with Local and Global Consistency

  2003cited by this paper
• Distance Metric Learning with Application to Clustering with Side-Information

  2002cited by this paper
• Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering

  2001cited by this paper
• Normalized cuts and image segmentation

  1997cited by this paper
• Constrained Spectral Clustering through Affinity Propagation

  year unknowncited by this paper

• No citing papers are available for this paper.