Providing a broad but in-depth introduction to neural network and machine learning in a
statistical framework, this book provides a single, comprehensive resource for study and …

We consider the problem of locality preserving projections (LPP) in two-dimensional sense.
Recently, LPP was proposed for dimensionality reduction, which can detect the intrinsic …

In this paper we propose a novel method combining graph embedding and difference
criterion techniques for image feature extraction, namely two-dimensional maximum …

The biologically inspired model, Hierarchical Model and X (HMAX), has excellent
performance in object categorization. It consists of four layers of computational units based …

This paper proposes a novel scheme for analog circuit fault diagnosis utilizing features
extracted from the time-frequency representations of signals and an improved vector-valued …

Two-dimensional local graph embedding discriminant analysis (2DLGEDA) and two-
dimensional discriminant locality preserving projections (2DDLPP) were recently proposed …

The matrix, as an extended pattern representation to the vector, has proven to be effective in
feature extraction. However, the subsequent classifier following the matrix-pattern-oriented …

This paper proposes a novel method of supervised and unsupervised multi-linear
neighborhood preserving projection (MNPP) for face recognition. Unlike conventional …

This paper proposes a novel method, called two-dimensional local graph embedding
discriminant analysis (2DLGEDA), for image feature extraction, which can directly extract the …

Two-dimensional canonical correlation analysis (2D-CCA) is an effective and efficient
method for two-view feature extraction and fusion. Since it is a global linear method, it fails to …