October 23: There will be no lecture on Tuesday, October 28. Please use the time to work on your final project. The TA, John, will be teaching the lecture on Thursday, Octorber 30. He will start talking about applications.
October 7: Two more handouts are given on matrix rank minimization.
September 29: A few handouts on almost Euclidean sections of L1 ball and related papers are posted below. There will be no lecture on Thursday. You need to work on your midterm project proposal. It is due on October 9th. Please turn in your proposal by Wednesday the 8th by email in pdf format, and prepare a short presentation, 5 minutes, for the class on Thursday the 9th.
September 15: A few more handouts are posted below. There will be no TA office hour this week.
September 6: Homework 1 is assigned below and due on Tuesday, September 16.
This course covers recent developments of the new mathematical theory of
sparse representation and compressed sensing in statistical signal processing, especially the concepts and
results that can be readily applied to pattern recognition, computer vision, and signal (image, speech) processing.
As this is a fastly evolving aera, it is my intension to study these new results together with all of the participants throughout the semester. So you do not have to be afraid that you do not know much about this topic, because neither do I.
Lectures on Discrete Geometry, Jiri Matousek, Springer, 2002.
Convex Polytope, Branko Grunbaum, Springer, 2002.
However, this course does not follow closely any textbook or lecture notes. Very often will we be presenting and discussing papers published recently on this topic. Related papers will be listed below.
Grading Policy: Homework & Paper Reading (60%), and Final Project (40%). The final project can
be done in a group of 2 or 3 students. The project can be theoretical, experimental or a mixture of
both. It consists of a midterm proposal, a final presentation (in class) and a web-based report.