IJCAI 2016 Tutorial

Title: Low-Rank and Sparse Modeling for Data Analytics [Slides]

Presenters: Sheng Li, Yun Fu

Abstract: Low-rank and sparse modeling are emerging mathematical tools dealing with uncertainties of real-world data. Leveraging on the underlying structure of data, low-rank and sparse modeling approaches have achieved impressive performance in many data analysis tasks. In this tutorial, we will introduce the background knowledge and optimization techniques, and review the existing algorithms of low-rank and sparse modeling. The algorithms cover a wide range of real-world applications such as clustering, semi-supervised classification, multi-task learning, transfer learning, collaborative filtering, and time-series analysis. This tutorial will conclude with a list of future research directions.

Time: 11:00AM - 12:45PM, July 11, 2016

Location: Sutton North, Hilton in midtown Manhattan, NYC

  • 11:00AM - 11:10AM: Introduction
  • 11:10AM - 11:30AM: Sparse and Low-Rank Representations
  • 11:30AM - 12:30PM: Algorithms and Models
    • Optimization algorithms
    • Scalable algorithms
    • Low-rank models for graph construction
    • Low-rank models for subspace learning
    • Low-rank models for domain adaptation
    • Low-rank models for multi-view learning
  • 12:30PM - 12:45PM: Applications and Conclusions