Face Recognition Using Nonlinear Partial Least Squares in Reproducing Kernel Hilbert Space

Hu, YeGang; Ren, Chuan-Xian; Yao, Yunfei; Li, Wanyi

doi:10.1007/978-3-642-33506-8_40

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Next, a well-known iterative algorithm (Lewi, 1995 ; Rosipal and Trejo, 2001 ; Hu et al, 2012 ) is used to optimize formula (1). A description in pseudo-code is shown in Algorithm 1:…”

Section: Methodsmentioning

confidence: 99%

“…Note that the variable K in Algorithm 1 is determined by the two-norm of the residual matrix of the data matrix X , that is, as long as the norm value is greater than a threshold, the program continues to cycle. In addition to being used for data reconstruction, PLS analysis can also be used effectively for dimensionality reduction, recognition, and regression (Rosipal and Trejo, 2001 ; Baek and Kim, 2004 ; Hu et al, 2012 ).…”

Section: Methodsmentioning

confidence: 99%

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Partial Least Square Aided Beamforming Algorithm in Magnetoencephalography Source Imaging

Yin

Zhang

et al. 2018

Front. Neurosci.

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Beamforming techniques have played a prominent role in source imaging in neuroimaging and in locating epileptogenic zones. However, existing vector-beamformers are sensitive to noise on localization of epileptogenic zones. In this study, partial least square (PLS) was used to aid the minimum variance beamforming approach for source imaging with magnetoencephalography (MEG) arrays, and verified its effectiveness in simulated data and epilepsy data. First, PLS was employed to extract the components of the MEG arrays by maximizing the covariance between a linear combination of the predictors and the class variable. Noise was then removed by reconstructing the MEG arrays based on those components. The minimum variance beamforming method was used to estimate a source model. Simulations with a realistic head model and varying noise levels indicated that the proposed approach can provide higher spatial accuracy than other well-known beamforming methods. For real MEG recordings in 10 patients with temporal lobe epilepsy, the ratios of the number of spikes localized in the surgical excised region to the total number of spikes using the proposed method were higher than that of the dipole fitting method. These localization results using the proposed method are more consistent with the clinical evaluation. The proposed method may provide a new imaging marker for localization of epileptogenic zones.

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“…Next, a well-known iterative algorithm (Lewi, 1995 ; Rosipal and Trejo, 2001 ; Hu et al, 2012 ) is used to optimize formula (1). A description in pseudo-code is shown in Algorithm 1:…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Partial Least Square Aided Beamforming Algorithm in Magnetoencephalography Source Imaging

Yin

Zhang

et al. 2018

Front. Neurosci.

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Assigning PLS Based Descriptors by SVM in Action Recognition

Sheng

Yang

et al. 2015

Lecture Notes in Computer Science

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Two dimensional non-negative sparse Partial Least Squares for face recognition

Huang

Feng

et al. 2014

2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

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The Partial Least Squares (PLS) algorithm has been widely applied in face recognition in recent years. However, all the improved algorithms of PLS did not utilize non-negativity and sparsity synchronously to improve the recognition accuracy and robustness. In order to solve these problems, this paper proposes a novel algorithm named TwoDimension Non-negative Sparse Partial Least Squares (2DNSPLS), which incorporates the constraints of nonnegativity and sparse to 2DPLS while extracting the facial features. Consequently, not only do the features extracted by 2DNSPLS contain the label information, as well as the internal structure of image matrix, but they also contain local non-negative interpretability and sparsity.For evaluating the approach's performance, a series of experiments are conducted on the Yale and the PIE face databases, which demonstrate that the proposed approach outperforms the state-of-art algorithms and has good robustness to occlusion.

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Face Recognition Using Nonlinear Partial Least Squares in Reproducing Kernel Hilbert Space

Cited by 3 publications

References 13 publications

Partial Least Square Aided Beamforming Algorithm in Magnetoencephalography Source Imaging

Partial Least Square Aided Beamforming Algorithm in Magnetoencephalography Source Imaging

Assigning PLS Based Descriptors by SVM in Action Recognition

Two dimensional non-negative sparse Partial Least Squares for face recognition

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