Multi-manifold matrix decomposition for data co-clustering
作者：
Highlights：
• We consider the geometric structures of both sample and feature manifolds.
• To reduces the complexity, we use two low-dimensional intermediate matrices.
• We employ multi-manifold learning to approximate the intrinsic manifold.
• The intrinsic manifold is constructed by linearly combining multiple manifolds.
• The candidate manifolds are constructed using six dimensionality reduction methods.
摘要
Highlights•We consider the geometric structures of both sample and feature manifolds.•To reduces the complexity, we use two low-dimensional intermediate matrices.•We employ multi-manifold learning to approximate the intrinsic manifold.•The intrinsic manifold is constructed by linearly combining multiple manifolds.•The candidate manifolds are constructed using six dimensionality reduction methods.
论文关键词：Co-clustering,Matrix Tri-Factorization,Muli-Manifold learning
论文评审过程：Received 24 March 2015, Revised 25 April 2016, Accepted 29 November 2016, Available online 1 December 2016, Version of Record 11 December 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.11.027
