Joint Dynamic Manifold and Discriminant Information Learning for Feature Extraction

A novel two-stage feature extraction scheme is proposed in this paper for eliciting discriminant information contained in the data from various nuclear power plant (NPP) sensors to facilitate event identification. Based on the idea of sensor type-wise block projection, the primal features can be extracted without losing the intrinsic structure contained in the multi-sensor data. The features are then subject to further dimensionality reduction by selecting the sensors that are most relevant to the events under consideration. Results from detailed experiments with data generated from a simulator of Taiwan Maanshan NPP illustrate the efficacy of the proposed scheme.

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A linear feature extraction for multiclass classification problems based on class mean and covariance discriminant information

IEEE Transactions on Pattern Analysis and Machine Intelligence ◽

10.1109/tpami.2006.26 ◽

2006 ◽

Vol 28 (2) ◽

pp. 223-235 ◽

Cited By ~ 32

Author(s):

Pi-Fuei Hsieh ◽

Deng-Shiang Wang ◽

Chia-Wei Hsu

Keyword(s):

Feature Extraction ◽

Multiclass Classification ◽

Classification Problems ◽

Linear Feature ◽

Linear Feature Extraction ◽

Multiclass Classification Problems ◽

Discriminant Information

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Feature Extraction by Dynamically Revising the Discriminant Information in the Objective Space

2008 Fourth International Conference on Natural Computation ◽

10.1109/icnc.2008.170 ◽

2008 ◽

Author(s):

Wenxin Yang ◽

Jina Wang ◽

Shuqin Rao ◽

Shaoe Xue ◽

Jian Yin

Keyword(s):

Feature Extraction ◽

Objective Space ◽

Discriminant Information

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Locality Preserving Maximum Scatter Difference Projection for Face Recognition

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.1179 ◽

2013 ◽

Vol 411-414 ◽

pp. 1179-1184

Author(s):

Yong Feng Qi ◽

Yuan Lian Huo

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Sample Space ◽

Face Database ◽

Experimental Conditions ◽

Feature Extraction Method ◽

Locality Preserving ◽

Nonlinear Distribution ◽

Discriminant Information ◽

Difference Projection

Maximum Scatter Difference (MSD) aims to preserve discriminant information of sample space, but it fails to find the essential structure of the samples with nonlinear distribution. To overcome this problem, an efficient feature extraction method named as Locality Preserving Maximum Scatter Difference (LPMSD) projection is proposed in this paper. The new algorithm is developed based on locality preserved embedding and MSD criterion. Thus, the proposed LPMSD not only preserves discriminant information of sample space but also captures the intrinsic submanifold of sample space. Experimental results on ORL, Yale and CAS-PEAL face database indicate that the LPMSD method outperforms the MSD, MMSD and LDA methods under various experimental conditions.

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An Improved Quantitative Image Analyser

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078663 ◽

1977 ◽

Vol 35 ◽

pp. 226-227

Author(s):

J.P. Fallon ◽

P.J. Gregory ◽

C.J. Taylor

Keyword(s):

Feature Extraction ◽

Quantitative Analysis ◽

Frequency Content ◽

General Sense ◽

Physical Measurements ◽

Quantitative Image ◽

Image Analyser ◽

Automatic Methods ◽

Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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