This article reports on the development of a collective low-rank subspace (CLRS) algorithm to deal with a problem in multiview data analysis
Multiview data are of great abundance in real-world applications, since various viewpoints and multiple sensors desire to represent the data in a better way. Conventional multiview learning methods aimed to learn multiple view-specific transformations meanwhile assumed that knowledge of training and test data were available in advance; however, they would fail when there is no prior knowledge for the probe data's view information, since the correct view-specific projections cannot be used to extract effective feature representations. The use of CLRS as reported in the current article attempts to reduce the semantic gap across multiple views by seeking a view-free, low-rank projection shared by multiple view-specific transformations. Moreover, the reported procedure exploits low-rank reconstruction to build a bridge between the view-specific features and those view-free ones transformed with the CLRS. Furthermore, a supervised cross-view regularizer was developed to couple the within-class data across different views to make the learned collective subspace more discriminative. The proposed CLRS makes the algorithm more flexible when addressing the challenging issue without any prior knowledge of the probe data's view information. Toward that end, two different settings of experiments on several multiview benchmarks were designed to evaluate the proposed approach. Experimental results have verified the effective performance of the proposed method by comparison with the state-of-the-art algorithms. (publisher abstract modified)