Multiset Canonical Correlations Using Globality Preserving Projections With Applications to Feature Extraction and Recognition

Multiset features extracted from the same patterns always represent different characteristics of data. Thus, it is very valuable to perform the extraction on multiple feature sets. This paper addresses the issue of multiset correlation feature extraction (MCFE) in multiple feature representations. A novel method is proposed to carry out the MCFE for classification, called multiset canonical correlations using globality-preserving projections (MCC-GPs), which can perform joint dimensionality reduction for high-dimensional data. MCC-GP integrates correlational characteristics of feature pairs and global geometric information of data in the transformed low-dimensional space. This makes MCC-GPs have better discriminant ability than a previous method proposed by the authors, called multiset integrated canonical correlation analysis (MICCA), which only considers correlations for recognition tasks. Furthermore, MCC-GP can subsume two popular feature extraction methods into its framework under some constraints. This also provides a new insight for these two methods. The proposed method is applied to pattern recognition and examined using the COIL-100 and ETH-80 object databases and AR, CMU PIE, and Yale face databases. Extensive experimental results show that MCC-GP outperforms MICCA and multiset canonical correlation analysis in terms of classification accuracy and efficiency.

I. Introduction

Feature extraction is an important technique for high-dimensional data, which can avoid the curse of dimensionality and improve computational efficiency in pattern classification. It has been widely used in many practical applications [1]–[3], such as, face recognition, image analysis, and so on. The main objective of feature extraction is to find meaningful low-dimensional representations of high-dimensional patterns such that the inherent data structures and relations are revealed. Two famous dimensionality reduction techniques, principal component analysis (PCA) [4] and linear discriminant analysis (LDA) [5], have been extensively used in face and digit image recognition [6]–[9].