2009
NeurIPS
NeurIPS 2009
Matrix Completion from Noisy Entries
Abstract
Given a matrix M of low-rank, we consider the problem of reconstructing it from noisy observations of a small, random subset of its entries. The problem arises in a variety of applications, from collaborative filtering (the ‘Netflix problem’) to structure-from-motion and positioning. We study a low complexity algorithm introduced in [1], based on a combination of spectral techniques and manifold optimization, that we call here OPTSPACE. We prove performance guarantees that are order-optimal in a number of circumstances.
🌉
Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics and Mathematics & Optimization
📈
Trend Setter
- Matrix Completion
🐣
Hot Topic Early Bird
- spectral method
Authors
Topics
Machine Learning > Core Methods > Regression
Machine Learning > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Application Areas > Efficient Computing
Data Science & Analytics > Applications > Recommender Systems
Mathematics & Optimization > Mathematics > Linear Algebra
Mathematics & Optimization > Optimization > Continuous Optimization
Machine Learning > Core Methods > Matrix Completion