Kernel principal component analysis for texture classification

Kim, K.I.; Park, S.H.; Kim, H.J.

doi:10.1109/97.895369

Cited by 39 publications

(18 citation statements)

References 2 publications

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“…KPCA is an improved approach based on PCA for nonlinear dimension reduction [8,10]. It operates through projecting signal sample x from n to N (N > n) dimensional space by an unknown mapping I, which makes the new function I(x) linearly separable in the N dimensional space.…”

Section: A Kpca Theorymentioning

confidence: 99%

Acoustic Emission Monitoring for Film Thickness of Mechanical Seals Based on Feature Dimension Reduction and Cascaded Decision

Zhang¹,

2014

2014 Sixth International Conference on Measuring Technology and Mechatronics Automation

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Mechanical seals operates by a thin fluid film to separate the pair of seal faces. The thickness of this film must be optimized for preventing the friction of two end faces and minimizing the leakage. In this study, the fluid film is measured by eddy current and acoustic emission techniques, and the data of eddy current are used to direct the processing of acoustic emission signal. To decrease the noise, wavelet packet and kernel principal component analysis are used to extract the data features. Then cascaded decision is presented to improve the recognition rate of artificial neural network, by which the film thickness can be estimated.

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Section: A Kpca Theorymentioning

confidence: 99%

Acoustic Emission Monitoring for Film Thickness of Mechanical Seals Based on Feature Dimension Reduction and Cascaded Decision

Zhang¹,

2014

2014 Sixth International Conference on Measuring Technology and Mechatronics Automation

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“…Recently, nonlinear scalespace approaches, such as kernel principle component analysis (KPCA) [26] and Laplacian Eigenmaps [27~29], have been considered as more efficient ways for discriminant information extraction. In this paper, we propose a reference frame subspace approach using Laplacian Eigenmap, which selects a limited number of reference frames to form a Laplacian Eigen subspace to measure the inter-frame dissimilarity.…”

Section: Fig 1 a Three Layer Video Abstraction Systemmentioning

confidence: 99%

“…Recent non-linear scale-space approaches [26][27][28][29][30] have recently been intensively researched. Among these approaches, Laplacian Eigenmap [27][28][29][30] has been considered among the best ways that can outperform the traditional linear SVD or LSA approaches.…”

Section: A Laplacian Eigenmapmentioning

confidence: 99%

Hierarchical video summarization in reference subspace

Jiang

Sadka

Crookes

2009

IEEE Trans. Consumer Electron.

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“…Examples of successful extensions in feature extraction include kernel principal component analysis (KPCA) [3], kernel discriminant analysis (KDA) [4,2,5], kernel-based orthogonal subspace projection [6], kernel Foley-Sammon optimal discriminant vectors [7], and kernel-based matched subspace detectors [8]. In addition to feature extraction, the kernel trick has also been applied to learning machines [9][10][11] and other applications, [12][13][14], such as blind source separation [15] and object tracking [16][17][18][19][20], to increase discrimination between the objects of interest. To evaluate the quality of a kernel space, a pool of useful distance measures have been derived and extended to their kernel versions, including Chernoff distance, Bhattacharyya distance, Kullback-Leibler divergence, Patrick-Fisher distance and Mahalanobis distance [21,22].…”

Section: Introductionmentioning

confidence: 99%