Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction

TL;DR

This paper introduces a novel method called CIFA for analyzing multi-block data, enabling the extraction of shared and unique features to improve classification and clustering performance.

Contribution

The paper proposes a new scheme for common and individual feature analysis in multi-block data, with algorithms for extracting shared bases and separating features.

Findings

CIFA effectively extracts common and individual features from synthetic and real data.

The method improves classification and clustering accuracy.

Experimental results outperform state-of-the-art techniques.

Abstract

Very often data we encounter in practice is a collection of matrices rather than a single matrix. These multi-block data are naturally linked and hence often share some common features and at the same time they have their own individual features, due to the background in which they are measured and collected. In this study we proposed a new scheme of common and individual feature analysis (CIFA) that processes multi-block data in a linked way aiming at discovering and separating their common and individual features. According to whether the number of common features is given or not, two efficient algorithms were proposed to extract the common basis which is shared by all data. Then feature extraction is performed on the common and the individual spaces separately by incorporating the techniques such as dimensionality reduction and blind source separation. We also discussed how the proposed CIFA can significantly improve the performance of classification and clustering tasks by exploiting common and individual features of samples respectively. Our experimental results show some encouraging features of the proposed methods in comparison to the state-of-the-art methods on synthetic and real data.