Discriminative graph regularized extreme learning machine and its application to face recognition

Peng, Yong; Wang, Suhang; Long, Xianzhong; Lu, Bao-Liang

doi:10.1016/j.neucom.2013.12.065

Cited by 105 publications

(44 citation statements)

References 37 publications

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“…Recent studies [22][23][24][25][26] have shown that learning performance can be greatly enhanced by considering the geometrical structure and local invariant idea [27]. It is obvious that this idea should be considered in both original data space and the reproducing kernel Hilbert space (RKHS).…”

Section: Introductionmentioning

confidence: 99%

Manifold Adaptive Kernelized Low‐Rank Representation for Semisupervised Image Classification

et al. 2018

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Constructing a powerful graph that can effectively depict the intrinsic connection of data points is the critical step to make the graph-based semisupervised learning algorithms achieve promising performance. Among popular graph construction algorithms, low-rank representation (LRR) is a very competitive one that can simultaneously explore the global structure of data and recover the data from noisy environments. Therefore, the learned low-rank coefficient matrix in LRR can be used to construct the data affinity matrix. Consider the existing problems such as the following: (1) the essentially linear property of LRR makes it not appropriate to process the possible nonlinear structure of data and (2) learning performance can be greatly enhanced by exploring the structure information of data; we propose a new manifold kernelized low-rank representation (MKLRR) model that can perform LRR in the data manifold adaptive kernel space. Specifically, the manifold structure can be incorporated into the kernel space by using graph Laplacian and thus the underlying geometry of data is reflected by the wrapped kernel space. Experimental results of semisupervised image classification tasks show the effectiveness of MKLRR. For example, MKLRR can, respectively, obtain 96.13%, 98.09%, and 96.08% accuracies on ORL, Extended Yale B, and PIE data sets when given 5, 20, and 20 labeled face images per subject.

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Section: Introductionmentioning

confidence: 99%

Manifold Adaptive Kernelized Low‐Rank Representation for Semisupervised Image Classification

et al. 2018

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“…Naturally, in ELM framework, it is necessary to introduce both properties to enhance its performance [21,22,26]. Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Though many ELM variants were proposed in the last few years [16][17][18][19][20][21]8], the extension on ELM research focused mainly on the supervised learning tasks. This greatly limits the applicability of ELM in utilizing unlabeled data.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised discriminative extreme learning machine and its applications to data clustering

Peng

Zheng

2016

Neurocomputing

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“…2) Graph regularized extreme learning machine: Graph regularized extreme Learning Machine (GELM) is based the idea that similar samples should share similar properties and through adding a graph regularization term on the objective of conventional ELM to ensure the output of samples from the same class should be similar [13]. The standard ELM with K hidden nodes with activation function g(x) can be modeled as following:…”

Section: ) Features Based On Statisticsmentioning

confidence: 99%

“…For the classification, Support vector machines (SVMs) have been extensively used in widespread applications and were proved to have good generalization ability. The discriminative graph regularized Extreme Learning Machine (GELM) also is used to improve the performance based on the idea that similar samples should share similar properties and were proved to achieve much performance gain over standard ELM [13]. Therefore, by using these classifiers, our proposed features and the existing wavelet energy features are evaluated respectively.…”

Section: Introductionmentioning

confidence: 99%

Recognizing slow eye movement for driver fatigue detection with machine learning approach

Jiao

Peng

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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Slow eye movement (SEM) regarded as a sign of onset of sleep is very significant for detecting driver fatigue, but its characteristics and detection algorithm have been rarely involved in the study of driver fatigue detection. In this study, some new features were extracted based on wavelet singularity analysis and statistics to detect SEMs. Six subjects participated in this simulated driving experiment, and for each subject, a more than 2 hours electro-oculogram (EOG) session was recorded. Each session was divided into SEM epochs and non-SEM epochs according to the common judgments made by the two of three experts by the visual recognition criteria of SEMs. Regarding the problem of detecting SEMs as an imbalance classification problem, and through the under-sampling and over-sampling methods a 2s horizontal electro-oculogram (HEO) signal could finally be recognized as the category of SEMs or non-SEMs with the classifiers SVM, GELM, and KNN respectively. Results prove that the proposed features was a little better than the wavelet energy features, and through the combination of the wavelet energy features and the new features based on wavelet singularity analysis and statistics, the classification results were improved obviously.

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Discriminative graph regularized extreme learning machine and its application to face recognition

Cited by 105 publications

References 37 publications

Manifold Adaptive Kernelized Low‐Rank Representation for Semisupervised Image Classification

Manifold Adaptive Kernelized Low‐Rank Representation for Semisupervised Image Classification

An unsupervised discriminative extreme learning machine and its applications to data clustering

Recognizing slow eye movement for driver fatigue detection with machine learning approach

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