Adaptive dimension reduction using discriminant analysis and <i>K</i> -means clustering

TLDR

The paper clarifies relationships among PCA, LDA, and K‑means clustering. The study proposes a framework that combines linear discriminant analysis with K‑means clustering to adaptively select the most discriminative subspace. The method integrates K‑means clustering to generate class labels with LDA for simultaneous subspace selection, allowing clustering and feature selection to occur together. Experiments on real‑world datasets demonstrate the effectiveness of the proposed framework.

Abstract

We combine linear discriminant analysis (LDA) and K-means clustering into a coherent framework to adaptively select the most discriminative subspace. We use K-means clustering to generate class labels and use LDA to do subspace selection. The clustering process is thus integrated with the subspace selection process and the data are then simultaneously clustered while the feature subspaces are selected. We show the rich structure of the general LDA-Km framework by examining its variants and their relationships to earlier approaches. Relations among PCA, LDA, K-means are clarified. Extensive experimental results on real-world datasets show the effectiveness of our approach.