Eigenspectra Versus Eigenfaces: Classification with a Kernel-Based Nonlinear Representor

Liu, Benyong; Zhang, Jing

doi:10.1007/11539087_83

Cited by 6 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This idea is firstly proposed by one of the authors of this article, and the extracted features by DFT plus PCA are called eigenspectra [14].…”

Section: Feature Extractionmentioning

confidence: 99%

“…and energy normalization using (1), and generate the matrix I. h) Re-estimate the model parameter λ using (12)- (14). i) Calculate P(O| λ 0 ) and P(O| λ ) respectively, if P(O| λ )> P(O| λ 0 ), then return to Step h. j) When P(O|λ (k+1) )-P(O|λ (k) )<ε, ε is a given threshold, λ (k) denotes the k-th estimated model.…”

Section: Algorithm A) Begin B) Divide Equally Each Training Image Imentioning

confidence: 99%

“…In feature extraction, PCA and the linear discriminant analysis (LDA) are two popular linear methods for dimension reduction and discriminative information representation [7] [13] [14]. However, PCA is very ineffective for extraction of the most discriminant features and LDA-based algorithms suffer from the so-called "small sample size" problem when the number of samples is far smaller than the dimensionality of samples.…”

Section: Feature Extractionmentioning

confidence: 99%

See 2 more Smart Citations

Face Recognition Based on PLS and HMM

Liu

2009

2009 Chinese Conference on Pattern Recognition

Self Cite

View full text Add to dashboard Cite

In this paper, a face recognition scheme is proposed, wherein face images are preprocessed based on pixel averaging and energy normalization, and features are extracted successively with fast Fourier transform and the partial least squares for dimensionality reduction, and classification results are obtained by a classifier based on hidden Markov model. Some experimental results on the Olivetti Research Laboratory face database are presented to show the feasibility of the presented scheme.

show abstract

“…This idea is firstly proposed by one of the authors of this article, and the extracted features by DFT plus PCA are called eigenspectra [14].…”

Section: Feature Extractionmentioning

confidence: 99%

Section: Algorithm A) Begin B) Divide Equally Each Training Image Imentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Face Recognition Based on PLS and HMM

Liu

2009

2009 Chinese Conference on Pattern Recognition

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ten different runs for different eigenspectra feature dimensions are performed [7], [16]. For each dimension, classification error rates are averaged over the ten runs and the forty subjects.…”

Section: An Application Experimentsmentioning

confidence: 99%

Kernel discrimination via oblique projection

Liu

2011

2011 International Conference on Image Analysis and Signal Processing

Self Cite

View full text Add to dashboard Cite

Classifier design plays a paramount role in pattern recognition. Previously, we set the problem in the framework of function approximation, wherein a classifier is assumed to be an element of a reproducing kernel Hilbert space continuously defined on the pattern feature space, and adopted orthogonal projection criteria for classifier design. In practice, subspaces spanned by features of different classes are not necessarily orthogonal to each other. And thus we consider here to discriminate a pattern class called the target class from other classes, by obliquely projecting a pattern feature vector onto the subspace spanned by the training pattern features of the target class, along the subspace spanned by those of other classes. In addition, we extend the discrimination algorithm to a nonlinear version using the related reproducing kernel. Experimental results on face recognition are presented to demonstrate the feasibility of the presented algorithm for pattern classification.

show abstract

“…KPCA enables this by using kernel methods and formulating PCA as the equivalent kernel eigenvalue problem. On account of the attractive capability, KPCA based methods have been extensively investigated [3], [4], [5], [6], and have showed excellent performance.…”

Section: Introductionmentioning

confidence: 99%

An Iterative Algorithm for Robust Kernel Principal Component Analysis

Wang

Pang

Shen

et al. 2007

2007 International Conference on Machine Learning and Cybernetics

View full text Add to dashboard Cite

Abstract:Principal Component Analysis (PCA) has been proven to be an efficient method in dimensionality reduction, feature extraction and pattern recognition. Kernel Principal Component Analysis (KPCA) can be considered as a natural nonlinear generalization of PCA, which performs linear PCA in a high dimensional space implicitly by using kernel trick. However, both conventional PCA and KPCA suffer from the deficiency of being sensitive to outliers. Existing robust KPCA has to eigen-decompose the Gram matrix directly in each step and is much more computationally infeasible due to the large size of the matrix when the number of training samples is large. By extending existing robust PCA algorithm using kernel methods, we present a novel robust adaptive algorithm for calculating the kernel principal components. The proposed method not only preserves the characteristic of capturing underlying nonlinear structure of KPCA but also is robust against outliers by restraining the effect of outlying samples. Compared with existing robust KPCA methods, our method is performed without having to store the kernel matrix, which can reduce significantly the storage burden. In addition, our method shows the potential of expansibility to the incremental learning version. Experimental results on synthetic data indicate that our improved algorithm is effective and promising.

show abstract

Eigenspectra Versus Eigenfaces: Classification with a Kernel-Based Nonlinear Representor

Cited by 6 publications

References 6 publications

Face Recognition Based on PLS and HMM

Face Recognition Based on PLS and HMM

Kernel discrimination via oblique projection

An Iterative Algorithm for Robust Kernel Principal Component Analysis

Contact Info

Product

Resources

About