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ELEC 631 - Advanced Digital Signal Processing
Streaming Algorithms
and
Dimensionality Reduction
Rice University
Spring 2009
- Background
This course covers a new approach to dealing with large data sets based on the concept of approximate "sketches". Sketches can be exponentially shorter than the original data yet retain important and useful information, including the number of distinct elements in the data set, the "similarity" between the data elements, and so on. Applications include data compression, approximate query processing in databases, network measurement, and signal processing/acquisition. - Topics include
Sketching, heavy hitters, sparse approximation, dimensionality reduction and the Johnson-Lindenstrauss lemma, compressive sensing. Useful tools we will see along the way include communication complexity, statistics, geometric functional analysis, and combinatorial group testing - Format
Our special guest Piotr Indyk will lecture in January and February. In March, students will read classic and recent papers and present to the rest of the class in a debate format. Students will also complete a group project.
- Open to
Students from any department with some background in probability and analysis
- Instructors
Piotr Indyk and Richard Baraniuk
Office Hours: by appointment
- Class meetings
Friday 2-5pm, 1044DH
- Readings (TBA)
- Grading
Class grade will be based on:
- class participation (20%)
- paper presentation (30%)
- group project (50%)
- class participation (20%)
- Owlspace
- Information on learning styles
