Self-Paced Multi-Task Clustering

TL;DR

This paper introduces a self-paced multi-task clustering framework that progressively learns from data, reducing local optima issues and noise sensitivity, and demonstrates improved performance over existing methods.

Contribution

The paper proposes a novel self-paced multi-task clustering approach that enhances robustness and avoids local optima through progressive data selection and a soft version for noise reduction.

Findings

Outperforms state-of-the-art multi-task clustering methods.

Converges reliably during optimization.

Effective in handling noisy and outlier data.

Abstract

Multi-task clustering (MTC) has attracted a lot of research attentions in machine learning due to its ability in utilizing the relationship among different tasks. Despite the success of traditional MTC models, they are either easy to stuck into local optima, or sensitive to outliers and noisy data. To alleviate these problems, we propose a novel self-paced multi-task clustering (SPMTC) paradigm. In detail, SPMTC progressively selects data examples to train a series of MTC models with increasing complexity, thus highly decreases the risk of trapping into poor local optima. Furthermore, to reduce the negative influence of outliers and noisy data, we design a soft version of SPMTC to further improve the clustering performance. The corresponding SPMTC framework can be easily solved by an alternating optimization method. The proposed model is guaranteed to converge and experiments on real data sets have demonstrated its promising results compared with state-of-the-art multi-task clustering methods.