Variational Graph Embedding for Globally and Locally Consistent Feature Extraction

Yang, Shuang-Hong; Zha, Hongyuan; Zhou, Sean X.; Hu, Bao-Gang

doi:10.1007/978-3-642-04174-7_35

Cited by 25 publications

(4 citation statements)

References 17 publications

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“…The FCSNMF model is solved by using the semi-quadratic method 10 , the ECM algorithm 11 and the multiplicative update rule method 12 . The following proposition 13 is first introduced in this paper before solving.…”

Section: Optimization Of the Fcsnmf Modelmentioning

confidence: 99%

Supervised nonnegative matrix factorization model with fused correntropy for tumor recognition

Wei

Liu

Yang

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Nonnegative matrix factorization (NMF) is a feature learning method that can achieve nonlinear dimensional approximate reduction with strong interpretation, and it is widely used in the field of tumor recognition. The objective function of the traditional NMF model is based on the Euclidean distance metric, and the performance of the model is easily affected by the noise. Moreover, traditional NMF is an unsupervised feature learning method that does not use the label information of the data. However, it would cause a waste of information without using label information and cannot learn the discriminative features in the data. Therefore, the supervised nonnegative matrix factorization model with fused correntropy (FCSNMF) is proposed in this paper. The FCSNMF model alleviates the effect of noise in the experimental data by fusing the Euclidean distance metric and the maximum correntropy metric. In addition, the label consistency regularization term is skillfully chosen to utilize the label information of the data to obtain discriminative features. The effectiveness of the FCSNMF model is verified by applying it to a gene expression profile dataset for tumor recognition.

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Section: Optimization Of the Fcsnmf Modelmentioning

confidence: 99%

Supervised nonnegative matrix factorization model with fused correntropy for tumor recognition

Wei

Liu

Yang

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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“…We can choose the free parameter s with the technique of simultaneous regression-scale estimation [48,49], Silverman's rule [50,34], or Huber's rule [51,52].…”

Section: Parameter Selection For Bpfsmentioning

confidence: 99%

Correntropy based feature selection using binary projection

Yan

Yuan

Yan

et al. 2011

Pattern Recognition

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“…However, the maximum correntropy in (11) is nonlinear, it is difficult to directly optimize. Recently, many researchers have made much effort to the half-quadratic technique [25], the expectationmaximization method [26], and the conjugate gradient algorithm [27], which are devoted to solve this optimization problem. In this paper, we use the half-quadratic technique to solve the optimization problem (11).…”

Section: Robust Psvr Based On Maximum Correntropy Criterionmentioning

confidence: 99%

Robust Proximal Support Vector Regression Based on Maximum Correntropy Criterion

Wang

Pei

Ding

et al. 2019

Scientific Programming

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The robustness problem of the classical proximal support vector machine for regression estimation (PSVR) when confronting with samples in the presence of outliers is addressed in this paper. Correntropy is a local similarity measure between two arbitrary variables and has been proven the insensitivity to noises and outliers. Based on the maximum correntropy criterion (MCC), a correntropy-based robust PSVR framework is proposed, named as RPSVR-MCC. The half-quadratic optimization method is employed to solve the resultant optimization, and an iterative algorithm is developed to solve RPSVR-MCC. In each iteration, the complex optimization can be converted to a linear system of equations which can be easily solved by the widely popular optimization techniques. The experimental results on synthetic datasets and real-world benchmark datasets demonstrate that the effectiveness of the proposed method. Moreover, the superiority of the proposed algorithm is more evident in noisy environment, especially in the presence of outliers.

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Variational Graph Embedding for Globally and Locally Consistent Feature Extraction

Cited by 25 publications

References 17 publications

Supervised nonnegative matrix factorization model with fused correntropy for tumor recognition

Supervised nonnegative matrix factorization model with fused correntropy for tumor recognition

Correntropy based feature selection using binary projection

Robust Proximal Support Vector Regression Based on Maximum Correntropy Criterion

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