S Chen, H Zhao, M Kong, B Luo - neurocomputing, 2007 - Elsevier
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 …
M Wan, M Li, G Yang, S Gai, Z Jin - Information sciences, 2014 - Elsevier
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 …
W He, Y He, Q Luo, C Zhang - Measurement Science and …, 2018 - iopscience.iop.org
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 …
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 …
M Wan, Z Lai, J Shao, Z Jin - Neurocomputing, 2009 - Elsevier
This paper proposes a novel method, called two-dimensional local graph embedding discriminant analysis (2DLGEDA), for image feature extraction, which can directly extract the …
X Gao, Q Sun, H Xu, Y Li - Neurocomputing, 2018 - Elsevier
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 …