A Unified Factors Analysis Framework for Discriminative Feature Extraction and Object Recognition

Abstract

Various methods for feature extraction and dimensionality reduction have been proposed in recent decades, including supervised and unsupervised methods and linear and nonlinear methods. Despite the different motivations of these methods, we present in this paper a general formulation known as factor analysis to unify them within a common framework. During factor analysis, an object can be seen as being comprised of content and style factors, and the objective of feature extraction and dimensionality reduction is to obtain the content factor without style factor. There are two vital steps in factor analysis framework; one is the design of factor separating objective function, including the design of partition and weight matrix, and the other is the design of space mapping function. In this paper, classical Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP) algorithms are improved based on factor analysis framework, and LDA based on factor analysis (FA-LDA) and LPP based on factor analysis (FA-LPP) are proposed. Experimental results show the superiority of our proposed approach in classification performance compared to classical LDA and LPP algorithms.

Groundwater chemical characteristic analysis and water source identification model study in Gubei coal mine, Northern Anhui Province, China

“…Factor analysis is a multivariate statistical analysis method used to transform a large number of variables that may be correlated with each other into a relatively small number of composite variables that are uncorrelated with each other [ 31 , 32 ]. The correlation coefficient matrix of the original variables is transformed through dimensionality reduction to obtain a few unobservable composite variables that can control all variables without losing valid information, and the composite variables are used to describe the correlations of the original variables.…”

Age Estimation of Face Images Based on CNN and Divide-and-Rule Strategy

“…In the factor analysis model (FAM) [36,37], it is desirable to find the optimal projection matrix to minimize the difference in style between homogeneous (same age) samples and to maximize the difference in content between different classes (different ages). We consider the features reflecting face of the characteristics of age changes as a content factor, which is defined as follows:…”