Su-Yun Huang

Orcid: 0000-0002-1602-2832

According to our database1, Su-Yun Huang authored at least 20 papers between 2004 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2022
Robust Self-Tuning Semiparametric PCA for Contaminated Elliptical Distribution.
IEEE Trans. Signal Process., 2022

Perturbation theory for cross data matrix-based PCA.
J. Multivar. Anal., 2022

Robust Aggregation for Federated Learning by Minimum γ-Divergence Estimation.
Entropy, 2022

2021
On the asymptotic normality and efficiency of Kronecker envelope principal component analysis.
J. Multivar. Anal., 2021

2020
On asymptotic normality of cross data matrix-based PCA in high dimension low sample size.
J. Multivar. Anal., 2020

TensorProjection Layer: A Tensor-Based Dimensionality Reduction Method in CNN.
CoRR, 2020

Altered Functional Complexity Associated with Structural Features in Schizophrenic Brain: A Resting-state fMRI Study.
Proceedings of the 5th International Conference on Complexity, 2020

2019
The generalized degrees of freedom of multilinear principal component analysis.
J. Multivar. Anal., 2019

2SDR: Applying Kronecker Envelope PCA to denoise Cryo-EM Images.
CoRR, 2019

2016
On the weak convergence and Central Limit Theorem of blurring and nonblurring processes with application to robust location estimation.
J. Multivar. Anal., 2016

2013
Asymptotic error bounds for kernel-based Nyström low-rank approximation matrices.
J. Multivar. Anal., 2013

Robust Independent Component Analysis via Minimum Gamma -Divergence Estimation.
IEEE J. Sel. Top. Signal Process., 2013

Statistical Analysis of Biomarkers for Personalized Medicine.
Comput. Math. Methods Medicine, 2013

2010
Multiclass support vector classification via coding and regression.
Neurocomputing, 2010

2009
Nonlinear Dimension Reduction with Kernel Sliced Inverse Regression.
IEEE Trans. Knowl. Data Eng., 2009

Robust Kernel Principal Component Analysis.
Neural Comput., 2009

A new regularized least squares support vector regression for gene selection.
BMC Bioinform., 2009

2007
Reduced Support Vector Machines: A Statistical Theory.
IEEE Trans. Neural Networks, 2007

Model selection for support vector machines via uniform design.
Comput. Stat. Data Anal., 2007

2004
Bayesian marginal inference via candidate's formula.
Stat. Comput., 2004


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