Multi-view Unsupervised Feature Selection by Cross-diffused Matrix Alignment
This addresses the problem of dimensionality reduction in multi-view learning for domains like big data, where class labels are scarce, but it is incremental as it builds on existing multi-view feature selection methods.
The paper tackles unsupervised feature selection for multi-view high-dimensional data by proposing CDMA-FS, which aligns cross-diffused matrices to better exploit multi-view information, and experiments on four real-world datasets show it outperforms state-of-the-art methods.
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.