Generalised nearest feature line for subspace learning

Pang, Yajun; Yuan, Yunhao; Li, Xuelong

doi:10.1049/el:20072176

Cited by 39 publications

(13 citation statements)

References 8 publications

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“…Using the tangent approximation technique could make the modeling of data variation more accurate, and it also has been shown as a promising technique for research in other fields [19,25]. Note that data variations can be derived from intra-class samples and extra-class samples [31].…”

Section: Tangent Approximationmentioning

confidence: 99%

Robust large margin discriminant tangent analysis for face recognition

Yang

Zheng

et al. 2011

Neural Comput & Applic

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Fisher's Linear Discriminant Analysis (LDA) has been recognized as a powerful technique for face recognition. However, it could be stranded in the non-Gaussian case. Nonparametric discriminant analysis (NDA) is a typical algorithm that extends LDA from Gaussian case to non-Gaussian case. However, NDA suffers from outliers and unbalance problems, which cause a biased estimation of the extra-class scatter information. To address these two problems, we propose a robust large margin discriminant tangent analysis method. A tangent subspace-based algorithm is first proposed to learn a subspace from a set of intra-class and extra-class samples which are distributed in a balanced way on the local manifold patch near each sample point, so that samples from the same class are clustered as close as possible and samples from different classes will be separated far away from the tangent center. Then each subspace is aligned to a global coordinate by tangent alignment. Finally, an outlier detection technique is further proposed to learn a more accurate decision boundary. Extensive experiments on challenging face recognition data set demonstrate the effectiveness and efficiency of the proposed method for face recognition. Compared to other nonparametric methods, the proposed one is more robust to outliers.

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Section: Tangent Approximationmentioning

confidence: 99%

Robust large margin discriminant tangent analysis for face recognition

Yang

Zheng

et al. 2011

Neural Comput & Applic

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“…An early improved method is nearest line (NL) classifier which was proposed by Li and Lu [21]. There are also many subsequent works to modify and improve the NL method [22][23][24][25][26]. The nearest plane (NP) classifier and nearest space (NS) classifier are proposed by Chien and Wu in [9] as the further extensions of NN and NL classifiers.…”

Section: Introductionmentioning

confidence: 99%

The nearest-farthest subspace classification for face recognition

Huang

Wang

et al. 2013

Neurocomputing

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“…To address this issue, a number of enhanced subspace learning algorithms based on the NFL metric have been proposed, recently. For example, Zheng et al proposed a nearest neighbour line nonparametric discriminant analysis (NFL-NDA) [9] algorithm, Pang et al presented a nearest feature line-based space (NFLS) [10] method, L. Yan proposed a neighborhood discriminant nearest feature line analysis (NDNFLA) [11].…”

Section: Introductionmentioning

confidence: 99%

Directional Discriminant Analysis for Image Feature Extraction

Zou

Zhao

2012

2012 Sixth International Conference on Genetic and Evolutionary Computing

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In this paper, two novel image feature extraction algorithms base on Directional Filter Banks are proposed, which are named Single Directional Two-Dimensional Linear Discriminant Analysis (SD2DLDA) and Multiple Directional Two-Dimensional Linear Discriminant Analysis (MD2DLDA). The SD2DLDA takes least running time while achieves high recognition rate and the MD2DLDA can get the higher recognition rate than other popular algorithms. Experimental results confirm the advantages of SD2DLDA and MD2DLDA.

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Generalised nearest feature line for subspace learning

Cited by 39 publications

References 8 publications

Robust large margin discriminant tangent analysis for face recognition

Robust large margin discriminant tangent analysis for face recognition

The nearest-farthest subspace classification for face recognition

Directional Discriminant Analysis for Image Feature Extraction

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