Affinity Weighted Embedding

Jason Weston, Ron Weiss, Hector Yee

Jan 19, 2013 (modified: Jan 19, 2013) ICLR 2013 conference submission readers: everyone
  • Abstract: Supervised (linear) embedding models like Wsabie and PSI have proven successful at ranking, recommendation and annotation tasks. However, despite being scalable to large datasets they do not take full advantage of the extra data due to their linear nature, and typically underfit. We propose a new class of models which aim to provide improved performance while retaining many of the benefits of the existing class of embedding models. Our new approach works by iteratively learning a linear embedding model where the next iteration's features and labels are reweighted as a function of the previous iteration. We describe several variants of the family, and give some initial results.
  • Decision: conferenceOral-iclr2013-workshop
  • Authorids: jweston@google.com, ronw@google.com, hector.yee@gmail.com

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