A Closed Form Solution to Multi-View Low-Rank Regression
This work addresses the challenge of integrating multiple data channels for improved regression performance, though it appears incremental by extending low-rank regression to multi-view settings.
The paper tackles the problem of learning from multi-view data by proposing a multi-view low-rank regression model, which outperforms single-view models on 4 datasets, demonstrating the utility of multi-view low-rank structure.
Real life data often includes information from different channels. For example, in computer vision, we can describe an image using different image features, such as pixel intensity, color, HOG, GIST feature, SIFT features, etc.. These different aspects of the same objects are often called multi-view (or multi-modal) data. Low-rank regression model has been proved to be an effective learning mechanism by exploring the low-rank structure of real life data. But previous low-rank regression model only works on single view data. In this paper, we propose a multi-view low-rank regression model by imposing low-rank constraints on multi-view regression model. Most importantly, we provide a closed-form solution to the multi-view low-rank regression model. Extensive experiments on 4 multi-view datasets show that the multi-view low-rank regression model outperforms single-view regression model and reveals that multi-view low-rank structure is very helpful.