Uncovering Locally Discriminative Structure for Feature Analysis

TL;DR

This paper introduces a semi-supervised feature learning method that combines manifold structure with local discriminative information, improving feature analysis by integrating local clique-based Fisher criteria and kernel extension.

Contribution

It proposes a novel approach that incorporates local discriminant information into manifold learning for enhanced semi-supervised feature analysis.

Findings

Outperforms existing methods on multiple datasets

Effectively integrates local discriminant info with manifold structure

Extends to high-dimensional feature spaces using kernels

Abstract

Manifold structure learning is often used to exploit geometric information among data in semi-supervised feature learning algorithms. In this paper, we find that local discriminative information is also of importance for semi-supervised feature learning. We propose a method that utilizes both the manifold structure of data and local discriminant information. Specifically, we define a local clique for each data point. The k-Nearest Neighbors (kNN) is used to determine the structural information within each clique. We then employ a variant of Fisher criterion model to each clique for local discriminant evaluation and sum all cliques as global integration into the framework. In this way, local discriminant information is embedded. Labels are also utilized to minimize distances between data from the same class. In addition, we use the kernel method to extend our proposed model and facilitate feature learning in a high-dimensional space after feature mapping. Experimental results show that our method is superior to all other compared methods over a number of datasets.