Two-dimensional discriminant locality preserving projections (2DDLPP) and its application to feature extraction via fuzzy set

Machine fault classification is an important task for intelligent identification of the health patterns for a mechanical system being monitored. Effective feature extraction of vibration data is very critical to reliable classification of machine faults with different types and severities. In this paper, a new method is proposed to acquire the sensitive features through a combination of local discriminant bases (LDB) and locality preserving projections (LPP). In the method, the LDB is employed to select the optimal wavelet packet (WP) nodes that exhibit high discrimination from a redundant WP library of wavelet packet transform (WPT). Considering that the obtained discriminatory features on these selected nodes characterize the class pattern in different sensitivity, the LPP is then applied to address mining inherent class pattern feature embedded in the raw features. The proposed feature extraction method combines the merits of LDB and LPP and extracts the inherent pattern structure embedded in the discriminatory feature values of samples in different classes. Therefore, the proposed feature not only considers the discriminatory features themselves but also considers the dynamic sensitive class pattern structure. The effectiveness of the proposed feature is verified by case studies on vibration data-based classification of bearing fault types and severities.

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A New Extension of Locality Preserving Projections for Image Feature Extraction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.241-244.1715 ◽

2012 ◽

Vol 241-244 ◽

pp. 1715-1718

Author(s):

Guo Hong Huang

Keyword(s):

Feature Extraction ◽

Local Information ◽

Image Feature ◽

Two Dimensional ◽

Two Stage ◽

Image Feature Extraction ◽

Locality Preserving ◽

Image Manifold ◽

New Framework ◽

Novel Algorithm

This paper proposes a novel algorithm for image feature extraction, namely, the two-directional two-dimensional locality preserving projection, ((2D)2LPP), which can find an embedding from two directions that not only preserves local information and detect the intrinsic image manifold structure, but also combines the both information between rows and those between columns simultaneously. We also combine the advantages of (2D)2LPP and LDA, and propose a new framework for feature extraction as two-stage: “(2D)2LPP+LDA.” The LDA step is performed to further reduce the dimension of feature matrix in the (2D)2LPP subspace. Experimental results on ORL face databases demonstrate the effectiveness of the proposed methods.

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Face Recognition Using Two-Dimensional Discriminant Locality Preserving Projections

2009 Asia-Pacific Conference on Information Processing ◽

10.1109/apcip.2009.137 ◽

2009 ◽

Cited By ~ 1

Author(s):

Yu Weiwei

Keyword(s):

Face Recognition ◽

Two Dimensional ◽

Locality Preserving Projections ◽

Locality Preserving

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Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.121-122.391 ◽

2010 ◽

Vol 121-122 ◽

pp. 391-398 ◽

Cited By ~ 2

Author(s):

Qi Rong Zhang ◽

Zhong Shi He

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Recognition Performance ◽

Recognition Rate ◽

Image Feature ◽

Two Dimensional ◽

Face Database ◽

Image Feature Extraction ◽

Locality Preserving Projection ◽

Locality Preserving

In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional locality discriminant preserving projections (2DLDPP). Two-dimensional locality preserving projections (2DLPP) can direct on 2D image matrixes. So, it can make better recognition rate than locality preserving projection. We investigate its more. The 2DLDPP is to use modified maximizing margin criterion (MMMC) in 2DLPP and set the parameter optimized to maximize the between-class distance while minimize the within-class distance. Extensive experiments are performed on ORL face database and FERET face database. The 2DLDPP method achieves better face recognition performance than PCA, 2DPCA, LPP and 2DLPP.

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