Kernel Discriminant Learning with Application to Face Recognition

Lu, Juwei; Venetsanopoulos, A.N.; Rogers, Edward S.

doi:10.1007/10984697_13

Cited by 8 publications

(9 citation statements)

References 37 publications

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“…In other words, we can observe the changes of the margins of the training examples at every boosting iteration, and consider it convergent when the margins of most training examples stop increasing or are increasing slowly. Our experiments indicate that this approach works well in many cases (see [51] for details). However, as mentioned earlier, the margin theory alone is insufficient to explain the behaviors of boosting [40], [45], [46].…”

Section: Some Discussion On the Convergence Of Boostingmentioning

confidence: 58%

“…It is therefore unrealistic to expect that the heuristic approach can accurately estimate the optimal value of . For example, it is found in our experiments that JD-LDA with the best found often yielded much better cumulative margin distributions than its boosting version [51].…”

Section: Some Discussion On the Convergence Of Boostingmentioning

confidence: 72%

“…Due to space limitations, only the results of B-JD-LDA and B-EFM with the PCDD are reported here (The interested readers can refer to [51] for the results obtained with ).…”

Section: Resultsmentioning

confidence: 99%

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Ensemble-based discriminant learning with boosting for face recognition

Plataniotis

Venetsanopoulos

et al. 2006

IEEE Trans. Neural Netw.

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174

134

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Abstract-In this paper, we propose a novel ensemble-based approach to boost performance of traditional Linear Discriminant Analysis (LDA)-based methods used in face recognition. The ensemble-based approach is based on the recently emerged technique known as "boosting." However, it is generally believed that boosting-like learning rules are not suited to a strong and stable learner such as LDA. To break the limitation, a novel weakness analysis theory is developed here. The theory attempts to boost a strong learner by increasing the diversity between the classifiers created by the learner, at the expense of decreasing their margins, so as to achieve a tradeoff suggested by recent boosting studies for a low generalization error. In addition, a novel distribution accounting for the pairwise class discriminant information is introduced for effective interaction between the booster and the LDA-based learner. The integration of all these methodologies proposed here leads to the novel ensemble-based discriminant learning approach, capable of taking advantage of both the boosting and LDA techniques. Promising experimental results obtained on various difficult face recognition scenarios demonstrate the effectiveness of the proposed approach. We believe that this work is especially beneficial in extending the boosting framework to accommodate general (strong/weak) learners.Index Terms-Boosting, face recognition (FR), linear discriminant analysis, machine learning, mixture of linear models, smallsample-size (SSS) problem, strong learner.

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Section: Some Discussion On the Convergence Of Boostingmentioning

confidence: 58%

Section: Some Discussion On the Convergence Of Boostingmentioning

confidence: 72%

See 1 more Smart Citation

Ensemble-based discriminant learning with boosting for face recognition

Plataniotis

Venetsanopoulos

et al. 2006

IEEE Trans. Neural Netw.

Self Cite

174

134

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“…Training vectors are mapped into a higher-dimensional space by a kernel function that describes a hyperplane, which consists of an optimal separation of the dataset into discrete classes according to the training samples. The four most widely used kernel functions are linear, polynomial, RBF, and sigmoid [77,78]. Table 3 lists the mathematical equations of these kernel functions.…”

Section: Support Vector Machinementioning

confidence: 99%

Comparative Analysis of MODIS Time-Series Classification Using Support Vector Machines and Methods Based upon Distance and Similarity Measures in the Brazilian Cerrado-Caatinga Boundary

et al. 2015

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Abstract:We have mapped the primary native and exotic vegetation that occurs in the Cerrado-Caatinga transition zone in Central Brazil using MODIS-NDVI time series (product MOD09Q1) data over a two-year period (2011)(2012)(2013). Our methodology consists of the following steps: (a) the development of a three-dimensional cube composed of the NDVI-MODIS time series; (b) the removal of noise; (c) the selection of reference temporal curves and classification using similarity and distance measures; and (d) classification using support vector machines (SVMs). We evaluated different temporal classifications using similarity and distance measures of land use and land cover considering several combinations of attributes. Among the classification using distance and similarity measures, the best result employed the Euclidean distance with the NDVI-MODIS data by considering more than one reference temporal curve per class and adopting six mapping classes. In the majority of tests, the SVM classifications yielded better results than other methods. The best OPEN ACCESSRemote Sens. 2015, 7 12161 result among all the tested methods was obtained using the SVM classifier with a fourth-degree polynomial kernel; an overall accuracy of 80.75% and a Kappa coefficient of 0.76 were obtained. Our results demonstrate the potential of vegetation studies in semiarid ecosystems using time-series data.

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“…The advantage of the QCQP is that it is faster than SDP and computationally affordable for moderate sized problems. Discriminant analysis provides an effective criterion to assess the feature space and consequently has been studied in various approaches to learn the kernel [15,55,46,89,94]. In [83], authors proposed an optimization approach that maximizes the linear discriminant analaysis's objective in the feature space which leads to finding the parameter of Gaussian kernel.…”

Section: Feature Space Conditionsmentioning

confidence: 99%

A survey of the state of the art in learning the kernels

2011

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In recent years, the machine learning community has witnessed a tremendous growth in the development of kernel-based learning algorithms. However, the performance of this class of algorithms greatly depends on the choice of the kernel function. Kernel function implicitly represents the inner product between a pair of points of a dataset in a higher dimensional space. This inner product amounts to the similarity between points and provides a solid foundation for nonlinear analysis in kernel-based learning algorithms. The most important challenge in kernel-based learning is the selection of an appropriate kernel for a given dataset. To remedy this problem, algorithms to learn the kernel have recently been proposed. These methods formulate a learning algorithm that finds an optimal kernel for a given dataset. In this paper, we present an overview of these algorithms and provide a comparison of various approaches to find an optimal kernel. Furthermore, a list of pivotal issues that lead to efficient design of such algorithms will be presented.

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Kernel Discriminant Learning with Application to Face Recognition

Cited by 8 publications

References 37 publications

Ensemble-based discriminant learning with boosting for face recognition

Ensemble-based discriminant learning with boosting for face recognition

Comparative Analysis of MODIS Time-Series Classification Using Support Vector Machines and Methods Based upon Distance and Similarity Measures in the Brazilian Cerrado-Caatinga Boundary

A survey of the state of the art in learning the kernels

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