Auto-weighted multi-view clustering via deep matrix decomposition
作者:
Highlights:
• Anovel deep multi-view learning model is proposed by uncovering the hierarchical semantics of the input data in a layer-wise way.
• The instances from the same class but from different views are forced to be closer layer by layer in the low-dimensional space, which is beneficial for the subsequent learning task.
• To automatically determine the weights of different views, we introduce the auto-weighting scheme into the deep multi-view clustering algorithm.
• To solve the optimization problem of our model, an efficient iterative updating algorithm is proposed with a theoretical guarantee of its convergence.
摘要
•Anovel deep multi-view learning model is proposed by uncovering the hierarchical semantics of the input data in a layer-wise way.•The instances from the same class but from different views are forced to be closer layer by layer in the low-dimensional space, which is beneficial for the subsequent learning task.•To automatically determine the weights of different views, we introduce the auto-weighting scheme into the deep multi-view clustering algorithm.•To solve the optimization problem of our model, an efficient iterative updating algorithm is proposed with a theoretical guarantee of its convergence.
论文关键词:Multi-view learning,Deep matrix decomposition,Clustering,Optimization algorithm
论文评审过程:Received 16 February 2019, Revised 8 August 2019, Accepted 17 August 2019, Available online 28 August 2019, Version of Record 30 August 2019.
论文官网地址:https://doi.org/10.1016/j.patcog.2019.107015