HSIC regularized manifold learning

[1]  Bernhard Schölkopf,et al.  Remote Sensing Feature Selection by Kernel Dependence Measures , 2010, IEEE Geoscience and Remote Sensing Letters.

[2]  Nicolas Courty,et al.  Sparse Hilbert Schmidt Independence Criterion and Surrogate-Kernel-Based Feature Selection for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Fakhri Karray,et al.  Multiview Supervised Dictionary Learning in Speech Emotion Recognition , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[4]  Hong Qiao,et al.  An Explicit Nonlinear Mapping for Manifold Learning , 2010, IEEE Transactions on Cybernetics.

[5]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[6]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[7]  Ivan Marsic,et al.  Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels , 2013, ICML.

[8]  Le Song,et al.  Feature Selection via Dependence Maximization , 2012, J. Mach. Learn. Res..

[9]  D. Donoho,et al.  Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Mehrdad J. Gangeh,et al.  Fast and Scalable Feature Selection for Gene Expression Data Using Hilbert-Schmidt Independence Criterion , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[11]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[12]  S. Sisson,et al.  A comparative review of dimension reduction methods in approximate Bayesian computation , 2012, 1202.3819.

[13]  N. Aronszajn.  Theory of Reproducing Kernels. , 1950 .

[14]  Mohamed S. Kamel,et al.  Kernelized Supervised Dictionary Learning , 2012, IEEE Transactions on Signal Processing.

[15]  Zoubin Ghahramani,et al.  Unifying linear dimensionality reduction , 2014, 1406.0873.

[16]  Zohreh Azimifar,et al.  Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds , 2011, Pattern Recognit..

[17]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Zhengming Ma,et al.  On the Equivalence of HLLE and LTSA , 2018, IEEE Transactions on Cybernetics.

[19]  H. Zha,et al.  Principal manifolds and nonlinear dimensionality reduction via tangent space alignment , 2004, SIAM J. Sci. Comput..