Multi-View representation learning in Multi-Task Scene

TL;DR

This paper introduces a semi-supervised multi-task multi-view learning algorithm that leverages common and special features across views to improve task performance and robustness to noise, validated through experiments.

Contribution

The paper proposes a novel semi-supervised algorithm, MTMVCSF, that effectively mines shared and view-specific features for multi-task multi-view learning, including an anti-noise variant.

Findings

Improved classification and clustering performance over raw data.

Effective handling of noisy labels with the anti-noise algorithm.

Validated results on real-world and synthetic datasets.

Abstract

Over recent decades have witnessed considerable progress in whether multi-task learning or multi-view learning, but the situation that consider both learning scenes simultaneously has received not too much attention. How to utilize multiple views latent representation of each single task to improve each learning task performance is a challenge problem. Based on this, we proposed a novel semi-supervised algorithm, termed as Multi-Task Multi-View learning based on Common and Special Features (MTMVCSF). In general, multi-views are the different aspects of an object and every view includes the underlying common or special information of this object. As a consequence, we will mine multiple views jointly latent factor of each learning task which consists of each view special feature and the common feature of all views. By this way, the original multi-task multi-view data has degenerated into multi-task data, and exploring the correlations among multiple tasks enables to make an improvement on the performance of learning algorithm. Another obvious advantage of this approach is that we get latent representation of the set of unlabeled instances by the constraint of regression task with labeled instances. The performance of classification and semi-supervised clustering task in these latent representations perform obviously better than it in raw data. Furthermore, an anti-noise multi-task multi-view algorithm called AN-MTMVCSF is proposed, which has a strong adaptability to noise labels. The effectiveness of these algorithms is proved by a series of well-designed experiments on both real world and synthetic data.