2017/05/02 by Xiaokai Wei, Wei, Xiaokai, Bokai Cao +3
Computer Science · Engineering · #Advanced Vision and Imaging #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1705.00825
openalex publication_date 2017/05/02 · openalex created_date 2017/05/12 · openalex updated_date 2026/07/28
Multi-view high-dimensional data become increasingly popular in the big data era. Feature selection is a useful technique for alleviating the curse of dimensionality in multi-view learning. In this paper, we study unsupervised feature selection for multi-view data, as class labels are usually expensive to obtain. Traditional feature selection methods are mostly designed for single-view data and cannot fully exploit the rich information from multi-view data. Existing multi-view feature selection methods are usually based on noisy cluster labels which might not preserve sufficient information from multi-view data. To better utilize multi-view information, we propose a method, CDMA-FS, to select features for each view by performing alignment on a cross diffused matrix. We formulate it as a constrained optimization problem and solve it using Quasi-Newton based method. Experiments results on four real-world datasets show that the proposed method is more effective than the state-of-the-art methods in multi-view setting.