Rank Aggregation via Nuclear Norm Minimization

Details Rank Aggregation via Nuclear Norm Minimization Rank Aggregation via Nuclear Norm Minimization

2011-02-23

arxiv.org/abs/1102.4821v1

Computer Science

Numerical Analysis

9 pages

Scientific paper

The process of rank aggregation is intimately intertwined with the structure of skew-symmetric matrices. We apply recent advances in the theory and algorithms of matrix completion to skew-symmetric matrices. This combination of ideas produces a new method for ranking a set of items. The essence of our idea is that a rank aggregation describes a partially filled skew-symmetric matrix. We extend an algorithm for matrix completion to handle skew-symmetric data and use that to extract ranks for each item. Our algorithm applies to both pairwise comparison and rating data. Because it is based on matrix completion, it is robust to both noise and incomplete data. We show a formal recovery result for the noiseless case and present a detailed study of the algorithm on synthetic data and Netflix ratings.

Affiliated with

Also associated with

No associations

Rating

Rank Aggregation via Nuclear Norm Minimization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Rank Aggregation via Nuclear Norm Minimization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Rank Aggregation via Nuclear Norm Minimization will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-175428