Null space discriminant locality preserving projections for face recognition

Yang, Liping; Gong, Weiguo; Gu, Xianfeng; Li, Weihong; Liang, Yixiong

doi:10.1016/j.neucom.2008.03.009

Cited by 56 publications

(14 citation statements)

References 14 publications

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“…KDA and its variations [23][24][25][26][27][28][29][30] only consider global geometrical structure and neglect local geometrical structure. In this section, we will develop a new KDA framework which can incorporate the local geometrical structure of data samples.…”

Section: Locality Preserving Kdamentioning

confidence: 99%

“…All such methods attempt to embed the original data into a submanifold by preserving the local geometrical structure. Different from LLE, Isomap and Laplacian eigenmap, LPP is a linear algorithm which is quite simple and easy to realize, thus has received much attention in the research community [23][24][25][26][27][28][29][30]. He et al [23] applied LPP on the face recognition and demonstrated the effectiveness of LPP in exploring the local geometrical structure of the data.…”

Section: Introductionmentioning

confidence: 99%

“…To use the class label information, Cai et al [27] proposed linear discriminant projection (LDP) method; Yang et al [28] developed multi-manifold discriminant analysis (MMDA) algorithm; Wong and Zhao [29] proposed supervised optimal locality preserving projection (SOLPP) and normalized Laplacian-based supervised optimal locality preserving projection (NL-SOLPP) methods; Masashi Sugiyama [47] proposed Local Fisher Discriminant Analysis (LFDA) method. To address the singularity problem, Yang et al [30] proposed a null space discriminant locality preserving projection for face recognition. The main drawback of their approach is the expensive computational cost caused by the singular value decomposition and eigenvalue decomposition in null space.…”

Section: Introductionmentioning

confidence: 99%

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Kernel Fisher Discriminant Analysis with Locality Preserving for Feature Extraction and Recognition

Zhang¹,

He²,

Zhao³

2013

IJCIS

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Many previous studies have shown that class classification can be greatly improved by kernel Fisher discriminant analysis (KDA) technique. However, KDA only captures global geometrical structure and disregards local geometrical structure of the data. In this paper, we propose a new feature extraction algorithm, called locality preserving KDA (LPKDA) algorithm. LPKDA first casts KDA as a least squares problem in the kernel space and then explicitly incorporates the local geometrical structure information into the least squares problem via regularization technique. The fact that LPKDA can make full use of two kinds of discriminant information, global and local, makes it a more powerful discriminator. Experimental results on four image databases show that LPKDA outperforms other kernel-based algorithms.

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Section: Locality Preserving Kdamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Kernel Fisher Discriminant Analysis with Locality Preserving for Feature Extraction and Recognition

Zhang¹,

He²,

Zhao³

2013

IJCIS

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show abstract

“…LPP finds an embedding space that preserves local information, and it is an unsupervised method. Many modified LPP algorithms have been put forward to consider the discriminant information of recognition task in recent years [30][31][32][33]. LPP is modeled based on the characterization of "locality".…”

Section: Introductionmentioning

confidence: 99%

Locally Minimizing Embedding and Globally Maximizing Variance: Unsupervised Linear Difference Projection for Dimensionality Reduction

Wan

Lai

2011

Neural Process Lett

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Recently, many dimensionality reduction algorithms, including local methods and global methods, have been presented. The representative local linear methods are locally linear embedding (LLE) and linear preserving projections (LPP), which seek to find an embedding space that preserves local information to explore the intrinsic characteristics of high dimensional data. However, both of them still fail to nicely deal with the sparsely sampled or noise contaminated datasets, where the local neighborhood structure is critically distorted. On the contrary, principal component analysis (PCA), the most frequently used global method, preserves the total variance by maximizing the trace of feature variance matrix. But PCA cannot preserve local information due to pursuing maximal variance. In order to integrate the locality and globality together and avoid the drawback in LLE and PCA, in this paper, inspired by the dimensionality reduction methods of LLE and PCA, we propose a new dimensionality reduction method for face recognition, namely, unsupervised linear difference projection (ULDP). This approach can be regarded as the integration of a local approach (LLE) and a global approach (PCA), so that it has better performance and robustness in applications. Experimental results on the ORL, YALE and AR face databases show the effectiveness of the proposed method on face recognition.

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“…So PCA approach, which discards some useful discriminatory information, is often used before DLPP. Yang et al [28] proposed a null space discriminant locality preserving projections (NDLPP) algorithms. However, NDLPP merely utilizes the discriminant information in the null space of the locality preserving within-class scatter.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal Complete Discriminant Locality Preserving Projections for Face Recognition

Lin

2011

Neural Process Lett

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In this paper, we propose a novel orthogonal complete discriminant locality preserving projections for facial feature extraction and recognition (OCDLPP). All training samples are projected into the range of a so-called locality preserving total scatter to reduce dimensionality without loss of discriminative information. The transformation matrix of OCDLPP is orthogonal and is found simultaneously using QR decomposition technique. Moreover, a feasible and effective procedure is proposed to alleviate the computational burden of high dimensional matrix for typical face image data. Experiments results on the ORL, Yale, FERET and PIE face databases show the effectiveness of the proposed OCDLPP.

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Null space discriminant locality preserving projections for face recognition

Cited by 56 publications

References 14 publications

Kernel Fisher Discriminant Analysis with Locality Preserving for Feature Extraction and Recognition

Kernel Fisher Discriminant Analysis with Locality Preserving for Feature Extraction and Recognition

Locally Minimizing Embedding and Globally Maximizing Variance: Unsupervised Linear Difference Projection for Dimensionality Reduction

Orthogonal Complete Discriminant Locality Preserving Projections for Face Recognition

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