Dates and Titles 
Topics 
Lecture Slides 
Suggested Further Readings 
Lecture 0
Welcome Introduction 



Lecture 1
Kernel PCA/ICA 

PCA

Kernel PCA

ICA

CCA

Kernel ICA


 B. Scholkopf, A.J. Smola, and K.R. Muller (1998),
"Nonlinear component analysis as a kernel eigenvalue problem,"
Neural Computation,
vol. 10, no. 5, pp. 12991319.

S. Choi (2009),
"Independent component analysis,"
Preprint to appear in Natural Computing.

D.R. Hardoon, S. Szedmak, and J. ShaweTaylor (2004),
"Canonical correlation analysis: An overview with application to learning
methods,"
Neural Computation,
vol. 16, pp. 26392664.

F.R. Bach and M.I. Jordan (2002),
"Kernel independent component analysis,"
Journal of Machine Learning Research,
vol. 3, pp. 148, 2002.

Lecture 2
Multidimensional scaling 

Metric MDS

Nonmetric MDS

Landmark MDS

Nystrom approximation


 T.F. Cox and M.A.A. Cox (2001),
Multidimensional Scaling,
Chapman and Hall.
 M. Steyvers (2002),
"Multidimensional scaling,"
Encyclopedia of Cognitive Science.
 V. de Silva and J.B. Tenenbaum (2004),
"Sparse multidimensional scaling using landmark points,"
Stanford Mathematics Technical Report, 2004.
 J. Platt (2005),
"FastMap, MetricMap, and Landmark MDS are all Nystrom algorithms,"
AISTATS2005.

Lecture 3
Manifold learning 

Isomap

Locally linear embedding

Laplacian eigenmap

Locality preserving projection


 J.B. Tenenbaum, V. De Silva, and J.C. Langford (2000),
"A global geometric framework for nonlinear
dimensionality reduction,"
Science, vol. 290, no. 5500, pp. 23192323.
 S. Roweis and L. Saul (2000),
"Nonlinear dimensionality reduction by locally linear embedding,"
Science, vol. 290, no. 5500, pp. 23232326.
 M. Belkin and P. Niyogi (2003),
"Laplacian eigenmaps for dimensionality reduction and
data representation,"
Neural Computation,
vol. 15, no. 6, pp. 13731396.

Lecture 4
Supervised embedding 

Marginal Fisher analysis

Local Fisher discriminant analysis


 S. Yan, D. Xu, B. Zhang, and H. J. Zhang (2005),
"Graph embedding: A general framework for dimensionality reduction,"
IEEE CVPR2005.
 H.T. Chen, H.W. Chang, and T.L. Liu (2005),
"Local discriminant embedding and its variants,"
IEEE CVPR2005.
 M. Sugiyama (2006),
"Local Fisher discriminant analysis for supervised dimensionality
reduction,"
ICML2006.

Lecture 5
Semisupervised learning 

Label propagation

Semisupervised learning on manifolds


 X. Zhu and Z. Ghahramani (2002),
"Learning from labeled and unlabeled data with label propagation,"
CMUCALD02107.
 X. Zhu, Z. Ghahramani, and J. Lafferty (2003) ,
"Semisupervised learning using Gaussian fields and harmonic functions,"
ICML2003.
 D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Scholkopf (2004),
"Learning with local and global consistency,"
NIPS2004.

M. Belkin and P. Niyogi (2004),
"Semisupervised learning on Riemannian manifolds,"
Machine Learning.
