Yiming Xue1, Nan Wang2, Yan Niu1, Ping Zhong2, ∗, Shaozhang Niu3, Yuntao Song4
CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 741-756, 2019, DOI:10.32604/cmc.2019.05611
Abstract In practical application, many objects are described by multi-view features because multiple views can provide a more informative representation than the single view. When dealing with the multi-view data, the high dimensionality is often an obstacle as it can bring the expensive time consumption and an increased chance of over-fitting. So how to identify the relevant views and features is an important issue. The matrix-based multi-view feature selection that can integrate multiple views to select relevant feature subset has aroused widely concern in recent years. The existing supervised multi-view feature selection methods usually concatenate all views into the long vectors… More >
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