Outlier Purusit: Robust PCA and Coolaborative Filtering

Principal Component Analysis is one of the most widely used techniques for dimensionality reduction. Nevertheless, it is plagued by sensitivity to outliers; finding robust analogs is critical. In the standard form, PCA involves organizing data into a matrix where columns represent the points, and rows the features. As such, outliers can be modeled as some columns that are completely arbitrary. We propose Outlier Pursuit – a recovery method based on convex optimization, and provide analytical guarantees on when it is able to both (a) recover the low-rank matrix, and (b) identify the outliers. Similar results can be obtained in the more challenging case where on top of having outliers, most entries of the matrix are missing. This can be used in the task of collaborative filtering where some “users” are malicious.

Speaker Details

I graduated from Shanghai Jiaotong University with a Bachelor’s degree in Automation in 1997, and got my Master’s degree in ECE from National University of Singapore in 2005. I was fortunate enough to obtain my Ph. D. degree in ECE from McGill University under the supervision of Shie Mannor, in 2009. I then spent two wonderful years in the WNCG group of UT-Austin, as a postdoctral research fellow working with Constantine Caramanis. I have been with the Department of Mechanical Engineering of NUS since 2011. Prior to starting my Ph.D sutdy, I have worked in Panasonic, HP and Oracle for almost six years.

My current research interest focus on learning and decision-making in large-scale complex systems. Specifically, I am interested in machine learning, high-dimensional statistics, robust and adaptable optimization, robust sequential decision making, and applications to large-scale systems.

Date:
Speakers:
Xu Huan
Affiliation:
National University of Singapore