A Closed Form Solution to Multi-View Low-Rank Regression

TL;DR

This paper introduces a novel multi-view low-rank regression model with a closed-form solution, effectively leveraging multi-modal data to improve regression performance across various datasets.

Contribution

It proposes the first closed-form solution for multi-view low-rank regression, extending previous single-view models to multi-view data.

Findings

Outperforms single-view regression models on multiple datasets

Demonstrates the effectiveness of low-rank constraints in multi-view settings

Shows that multi-view low-rank structure enhances learning performance

Abstract

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.