Material Detail

Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries

Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries

This video was recorded at 25th Annual Conference on Neural Information Processing Systems (NIPS), Granada 2011. Learning sparse representations on data adaptive dictionaries is a state-of-the-art method for modeling data. But when the dictionary is large and the data dimension is high, it is a computationally challenging problem. We explore three aspects of the problem. First, we derive new, greatly improved screening tests that quickly identify codewords that are guaranteed to have zero weights. Second, we study the properties of random projections in the context of learning sparse representations. Finally, we develop a hierarchical framework that uses incremental random projections and screening to learn, in small stages, a hierarchically structured dictionary for sparse representations.... Show More
Rate

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Browse...

Disciplines with similar materials as Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.
hidden