Semi-supervised Feature Analysis by Mining Correlations among Multiple Tasks

TL;DR

This paper introduces a semi-supervised feature selection method that leverages correlations among multiple tasks and unlabeled data, improving performance in multimedia applications.

Contribution

It presents a novel multi-task semi-supervised feature selection framework that considers feature correlations and shared structures across tasks.

Findings

Outperforms state-of-the-art feature selection algorithms

Effectively utilizes both labeled and unlabeled data

Demonstrates improved performance across multimedia applications

Abstract

In this paper, we propose a novel semi-supervised feature selection framework by mining correlations among multiple tasks and apply it to different multimedia applications. Instead of independently computing the importance of features for each task, our algorithm leverages shared knowledge from multiple related tasks, thus, improving the performance of feature selection. Note that we build our algorithm on assumption that different tasks share common structures. The proposed algorithm selects features in a batch mode, by which the correlations between different features are taken into consideration. Besides, considering the fact that labeling a large amount of training data in real world is both time-consuming and tedious, we adopt manifold learning which exploits both labeled and unlabeled training data for feature space analysis. Since the objective function is non-smooth and difficult to solve, we propose an iterative algorithm with fast convergence. Extensive experiments on different applications demonstrate that our algorithm outperforms other state-of-the-art feature selection algorithms.