Exponential locality preserving projections for small sample size problem

Wang, Sujing; Chen, Huiling; Peng, Xinhua; Zhou, Chunguang

doi:10.1016/j.neucom.2011.07.007

Cited by 48 publications

(35 citation statements)

References 16 publications

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“…It aims to build an approximate kNN graph with linear time complexity, which is favorable for large-scale hashing. The conventional manifold learning methods (e.g., [47], [48], [49]) build an exact kNN graph, which have at least quadratic time complexity and are prohibitive for large-scale hashing applications.…”

Section: Algorithm 1: the Algorithm Of E-selve: Training Stagementioning

confidence: 99%

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A Sparse Embedding and Least Variance Encoding Approach to Hashing

Zhu

Zhang

Huang

2014

IEEE Trans. on Image Process.

216

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Abstract-Hashing is becoming increasingly important in large-scale image retrieval for fast approximate similarity search and efficient data storage. Many popular hashing methods aim to preserve the kNN graph of high dimensional data points in the low dimensional manifold space, which is however difficult to achieve when the number of samples is big. In this paper, we propose an effective and efficient hashing approach by sparsely embedding a sample in the training sample space and encoding the sparse embedding vector over a learned dictionary. To this end, we partition the sample space into clusters via a linear spectral clustering method, and then represent each sample as a sparse vector of normalized probabilities that it falls into its several closest clusters. This actually embeds each sample sparsely in the sample space. The sparse embedding vector is employed as the feature of each sample for hashing. We then propose a least variance encoding model, which learns a dictionary to encode the sparse embedding feature, and consequently binarize the coding coefficients as the hash codes. The dictionary and the binarization threshold are jointly optimized in our model. Experimental results on benchmark datasets demonstrated the effectiveness of the proposed approach in comparison with stateof-the-art methods.Index Terms-hashing, manifold learning, image retrieval, dictionary learning.

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Section: Algorithm 1: the Algorithm Of E-selve: Training Stagementioning

confidence: 99%

“…For the ESE step in E-SELVE, the parameter γ is also set by line search over γ = [0.01, 0.1, 1, 10, 100]. In the experiments, we vary the length of hash codes (i.e., number of hash bits) as [12,16,24,28,32,48,64]. The sensitivity of the proposed method to these parameters will be discussed in Section III-E.…”

Section: Parameter Settingmentioning

confidence: 99%

A Sparse Embedding and Least Variance Encoding Approach to Hashing

Zhu

Zhang

Huang

2014

IEEE Trans. on Image Process.

216

View full text Add to dashboard Cite

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“…Then, the EDA method is introduced to solve this problem. The exponential LPP (ELPP) [9], the exponential DLPP method (EDLPP) [10], the exponential LDE method (ELDE) [11] and the exponential SDE method (ESDE) [12] are proposed. They are the exponential versions of the corresponding methods.…”

Section: Introductionmentioning

confidence: 99%

A Simple and Effective Generalization of Exponential Matrix Discriminant Analysis and Its Application to Face Recognition

Ran

Fang

2018

IEICE Trans. Inf. & Syst.

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SUMMARYAs an effective method, exponential discriminant analysis (EDA) has been proposed and widely used to solve the so-called smallsample-size (SSS) problem. In this paper, a simple and effective generalization of EDA is presented and named as GEDA. In GEDA, a general exponential function, where the base of exponential function is larger than the Euler number, is used. Due to the property of general exponential function, the distance between samples belonging to different classes is larger than that of EDA, and then the discrimination property is largely emphasized. The experiment results on the Extended Yale and CMU-PIE face databases show that, GEDA gets more advantageous recognition performance compared to EDA. key words: matrix exponential, linear discriminant analysis, the small sample size problem, face recognition

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“…L. Zhu et al [27] propose the orthogonal discriminant locality preserving projections (ODLPP) by orthogonalizing the basis vectors. S. J. Wang et al [28] present an exponential locality preserving projections (ELPP) via introducing the matrix exponential function. The above methods can obtain impressive results.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal maximum margin projection subspace for radar target HRRP recognition

Zhou

2016

J Wireless Com Network

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In this paper, a novel target recognition method, namely orthogonal maximum margin projection subspace (OMMPS), is proposed for radar target recognition using high-resolution range profile (HRRP). The core of OMMPS is to maximize the between-class margin by increasing the between-class scatter distance and reducing the within-class scatter distance simultaneously. By introducing the nonlinear mapping function, we also derive the kernel version of OMMPS, namely orthogonal kernel maximum margin projection subspace (OKMMPS). Compared with maximum margin criterion (MMC) method, OMMPS are optimal in meaning of maximum margin due that the coordinate axes of OMMPS are obtained sequentially by solving the constrained optimization problem, thus improves the recognition performance. In addition, the number of efficient features for OMMPS is not limited by the number of pattern classes, and the appropriate features can still be obtained for separating the classes, even in high-dimensional space with only a few classes. Moreover, the coordinate axes of OMMPS are mutually orthogonal, and the features extracted by OMMPS reduce the redundancy. The extensive experimental results show that the proposed method has better recognition performance than the other methods such as MMC and LDA.

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Exponential locality preserving projections for small sample size problem

Cited by 48 publications

References 16 publications

A Sparse Embedding and Least Variance Encoding Approach to Hashing

A Sparse Embedding and Least Variance Encoding Approach to Hashing

A Simple and Effective Generalization of Exponential Matrix Discriminant Analysis and Its Application to Face Recognition

Orthogonal maximum margin projection subspace for radar target HRRP recognition

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