Dimensionality Reduction -- Spring 2010
Students should check these pages at least once a week
for important information.
This course will review computational methods for reducing the
dimensionality of high dimensional data which lie on or near a
manifold of low intrinsic dimensionality. Topics will include: linear
methods (such as principal components analysis, factor analysis,
singular value decomposition); classic visualizations methods (such as
multidimensional scaling and its non-metric variants); and more recent
methods based on eigenvectors of Laplacians and convex optimization
(such as Kernel PCA, Locally Linear Embedding, Isomap and Maximum
Variance Unfolding). Both theoretical and algorithmic properties of
the methods will be discussed. Coursework will include small scale
computational experiments and readings of primary source research
- First class is Thursday January 21, 1:30-3:20pm in 719Broadway, room 1221.
Instructor: Sam Roweis
Course Email List:: g22_3033_012_sp10 at cs dot nyu dot edu
Please do NOT send instructors or tutors email about the class
directly to their personal accounts.
They are not able to answer class email
except to ???? at cs dot toronto dot edu.
Lectures: Thursdays 1:30-3:20pm, 719 Broadway, Room 1221.
Office Hours: Thursdays 3:30-4:30pm or by appointment, 719 Broadway, Room 1206.
Course Information |
Lecture Schedule/Notes |
G22-3033 - Dimensionality Reduction || www.cs.nyu.edu/~roweis/dimension_reduction/