陳 素 雲
Su-Yun Huang
Research Fellow
Institute of Statistical Science,
Academia Sinica
Taipei 11529, Taiwan
Tel: 886-2-27871965
Fax: 886-2-27831523
syhuang@stat.sinica.edu.tw
Education
Ph.D.
in Statistics (1990), Purdue University ,
Advisor: William J. Studden.
Thesis: Density Estimation by Spline Projection Kernels.
M.S. in Mathematics(1985), National
Taiwan University,
B.S. in
Mathematics(1983), National Taiwan University,
Experience
2004-present Research Fellow, Institute of Statistical Science,
Academia Sinica.
1997-2004 Associate Research Fellow, Institute
of Statistical Science, Academia Sinica.
1993-1997 Assistant Research Fellow, Institute
of Statistical Science, Academia Sinica.
1990-1993 Assistant Professor, Department of Mathematics, Wayne
State University, U.S.A.
Research Interests
Dimension reduction, high-dimensional data analysis,
machine learning & deep learning, robust statistical inference and
algorithms.
Full
Publication List
1. Lu,
R.S., Wang, S.H. and Huang, S.Y. (2024). A geometric algorithm for contrastive
principal component analysis in high
dimension. Journal of Computational and Graphical
Statistics, published online, https://doi.org/10.1080/10618600.2023.2289542
2. Hsiung,
S.Y., Deng, S.X., Li, J., Huang, S.Y., Liaw, C.K., Huang, S.Y., Wang, C.C. and
Hsieh, Y. (2023). Machine learning-based monosaccharide profiling for
tissue-specific classification of Wolfiporia extensa samples. Carbohydrate Polymers, 322, 121338.
3. Hung,
H., Huang, S.Y., and Eguchi, S. (2022). Robust self-tuning semiparametric PCA
for contaminated elliptical distribution. IEEE
Transactions on Signal Processing, 70, 5885-5897.
4. Li,
C.J., Huang, P.H., Ma, Y.T., Hung, H. and Huang, S.Y. (2022). Robust
aggregation for federated learning by minimum γ-divergence estimation. Entropy, 24, 686.
5. Wang,
S.H. and Huang, S.Y. (2022). Perturbation theory for cross data matrix-based
PCA. J. Multivariate Analysis, 190,
104960.
6. Hung,
H., Huang, S.Y., Ing, C.K. (2022). A generalized information criterion for
high-dimensional PCA rank
selection. Statistical Papers, 1-27.
7. Lee
Y.J., Huang, S.Y., Lin, C.P., Tsai, S.J. and Yang, A.C. (2021). Alteration of
power law scaling of spontaneous brain activity in schizophrenia. Schizophrenia Research, 238, 10-19.
8. Chen,
P.T., Chang, D., Yen, H., Liu, K.L., Huang, S.Y., Roth, H., Wu, M.H., Liao,
W.C. and Wang, W. (2021). Radiomic features on CT can distinguish pancreatic
cancer from noncancerous pancreas. Radiology:
Imaging Cancer, Vol.3, Issue 4.
9. Huang,
S. and Huang, S.Y. (2021). On the asymptotic normality and efficiency of
Kronecker envelope principal component analysis. J. Multivariate Analysis,
184, 104761.
10. Chen,
T.L., Huang, S.Y. and Wang, W. (2020). A consistency theorem for randomized
singular value decomposition. Statistics and Probability Letters, 161,
108743.
11. Cheng,
Y.H., Huang, T.M. and Huang, S.Y. (2020). Tensor decomposition for dimension
reduction. WIREs Computational Statistics, 12:e1482.
12. Chung,
S.C., Wang, S.H., Niu, P.Y., Huang, S.Y., Chang, W.H. and Tu, I.P. (2020).
Twostage dimension reduction for noisy high-dimensional images and application
to Cryogenic Electron Microscopy. Annals of Mathematical Sciences and
Applications, 5, 283-316.
13. Wang,
S.H., Huang, S.Y. and Chen, T.L. (2020). On asymptotic normality of cross data
matrix-based PCA in high dimension low sample size. J. Multivariate Analysis,
175, 104556.
14. Hung,
H. and Huang, S.Y. (2019). Sufficient dimension reduction via random partitions
for large-p-small-n problems. Biometrics, 75(1), 245-255.
15. Tu, I.P., Huang, S.Y. and Hsieh, D.N. (2019).
The generalized degrees of freedom of multilinear principal component analysis.
J. Multivariate Analysis, 173, 26-37.
16. Hung,
H., Jou, Z.Y. and Huang, S.Y. (2018). Robust mislabel logistic regression
without modeling mislabel probabilities. Biometrics, 74, 145-154.
17. Chen,
T.L., Fujisawa, H., Huang, S.Y. and Huang, C.R. (2016). On the weak convergence
and central limit theorem of blurring and nonblurring processes with
application to robust location estimation. J. Multivariate Analysis,
143, 165-184.
18. Hung,
H., Lin, Y.T., Chen, P.W., Wang, C.C., Huang, S.Y. and Tzeng, J.Y. (2016).
Detection of gene-gene interactions using multistage sparse and low-rank
regression. Biometrics, 72, 85-94.
19. Chen,
T.L., Hsieh, D.N., Hung, H., Tu, I.P.,Wu, P.S., Wu, Y.M., Chang, W.H. and
Huang, S.Y. (2014). gamma-SUP: a self-updating clustering algorithm based on minimum
gammadivergence with application to cluster cryo-EM images of asymmetric
particles. Annals of Applied Statistics, 8, 259-285.
20. Chen,
T.L., Huang, S.Y., Hung, H. and Tu, I.P. (2014). An introduction to multilinear
principal component analysis. J. Chinese Statistical Association,
special issue on Machine Learning, 52, 24-43.
21. Yeh,
Y.R., Huang, S.Y., Pao, H.K. and Lee, Y.J. (2014). A review of reduced kernel
trick in machine learning. J. Chinese Statistical Association, special
issue on Machine Learning, 52, 85-114.
22. Chen,
P., Hung, H., Komori, O., Huang, S.Y. and Eguchi, S. (2013). Robust independent
component analysis via minimum gamma-divergence estimation. IEEE Journal of
Selected Topics in Signal Processing, 7, 614-624.
23. Chang,
L.B., Bai, Z.D., Huang, S.Y. and Hwang, C.R. (2013). Asymptotic error bounds
for kernel-based Nystrőm low-rank approximation matrix. Journal
of Multivariate Analysis, 120, 102-119.
24. Hung,
H., Wu, P.S., Tu, I.P. and Huang, S.Y. (2012). On multilinear principal
component analysis of order-two tensors. Biometrika, 99, 569-583.
25. Lee,
M.H., Tzeng, J.Y., Huang, S.Y. and Hsiao, C.K. (2011). Combining an
evolution-guided clustering algorithm and haplotype-based LRT in family
association studies. BMC Genetics, 12:48.
26. Chen,
P.C., Lee, K.Y., Lee, T.J., Lee, Y.J. and Huang, S.Y. (2010). Multiclass
support vector classifcation via coding and regression. Neurocomputing,
73, 1501-1512.
27. Huang,
S.Y., Lee, M.H. and Hsiao, C.K. (2009). Nonlinear measures of association with
kernel canonical correlation analysis and applications. J. Statist. Planning
Inference, 139, 2162-2174.
28. Yeh,
Y.R., Huang, S.Y. and Lee, Y.J. (2009). Nonlinear dimension reduction with
kernel sliced inverse regression. IEEE Trans. Knowledge and Data Engineering,
21, 1590-1603.
29. Huang,
S.Y. Yeh, Y.R. and Eguchi, S. (2009). Robust kernel principal component
analysis. Neural Computation, 21, 3179-3213.
30. Chen,
P.C., Huang, S.Y., Chen, W.J. and Hsiao, C.K. (2009). A new regularized least
squares support vector regression for gene selection. BMC Bioinformatics,
10:44.
31. Huang,
C.M., Lee, Y.J., Lin, D. and Huang, S.Y. (2007). Model selection for support
vector machine via uniform design. Computational Statistics and Data
Analysis, 52, 335-346.
32. Lee, Y.J.
and Huang, S.Y. (2007). Reduced support vector machines: a statistical theory. IEEE
Trans. Neural Networks, 18, 1-13.
33. Wang,
C., Tsai, M.Y., Lee, M.H., Huang, S.Y., Kao, C.H., Ho, H.N. and Hsiao, C.K.
(2007).Maximum number of live births per donor in artificial insemination. Human
Reproduction, 22, 1363-1372.
34. Hsiao,
C.K., Huang, S.Y. and Chang, C.W. (2004). Bayesian marginal inference via
Candidate's formula. Statistics and Computing, 14, 59-66.
35. Lin,
M.H, Huang, S.Y. and Chang, Y.C. (2004). Kernel-based discriminant techniques
for educational placement. J. Educational & Behavioral Statistics,
29, 219-240.
36. Huang,
S.Y., Hsiao, C.K. and Chang, C.W. (2003). Optimal volume-corrected Laplace
Metropolis method. Ann. Inst. Statist. Math., 55, 655-670.
37. Lu., H.S.,
Huang, S.Y. and Lin, F.J. (2003). Generalized cross-validation for wavelet
shrinkage in nonparametric mixed-effects models. J. Computational and
Graphical Statistics, 12, 714-730.
38. Huang,
S.Y. (2002). On a Bayesian aspect for soft wavelet shrinkage estimation under
an asymmetric linex loss. Statistics and Probability Letters, 56,
171-175.
39. Huang,
S.Y. and Lu, H.S. (2001). Extended Gauss-Markov theorem for nonparametric
mixedeffects models. J. Multivariate Analysis, 76, 249-266.
40. Huang,
S.Y. and Lu, H.S. (2000). Bayesian wavelet shrinkage for nonparametric
mixed-effects models. Statistica Sinica, 10, 1021-1040.
41. Chow,
Y.S. and Huang, S.Y. (1999). A characterization of the uniform distribution via
moments of n-fold convolution modulo one. Sankhyā A, 61, 148-151.
42. Huang,
S.Y. (1999). Density estimation by wavelet-based reproducing kernels. Statistica
Sinica, 9, 137-151.
43. Huang,
I.C. and Huang, S.Y. (1999). Bernoulli numbers and polynomials via residues. J.
Number Theory, 76, 178-193.
44. Huang,
S.Y. (1997). Wavelet based empirical Bayes estimation for the uniform
distribution. Statistics and Probability Letters, 32, 141-146.
45. Huang,
S.Y. and Liang, T. (1997). Empirical Bayes estimation of the truncation
parameter with Linex loss. Statistica Sinica, 7, 755-769.
46. Huang,
S.Y. (1996). On the consistency of hierarchical Bayes estimators. Statistics
and Decisions, 14, 295-305.
47. Huang,
S.Y. (1995). Empirical Bayes testing procedures in some nonexponential families
using asymmetric Linex loss function. J. Statistical Planning and Inference,
46, 293-309.
48. Huang,
S.Y. and Studden, W.J. (1993). Density estimation using spline projection
kernels. Communications in Statistics-Theory and Methods, 22, 3263-3285.
49. Huang,
S.Y. and Studden, W.J. (1993). An equivalent kernel method for least square
spline regression. Statistics and Decisions, supp. 3, 179-201.
Last
Update: 2024-05-27 11:18 AM