Affinity Weighted Embedding
Venue
International Conference on Machine Learning (2014)
Publication Year
2014
Authors
Jason Weston, Ron Weiss, Hector Yee
BibTeX
Abstract
Supervised linear embedding models like Wsabie (Weston et al., 2011) and supervised
semantic indexing (Bai et al., 2010) 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 we believe they 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 approach works by reweighting each
component of the embedding of features and labels with a potentially nonlinear
affinity function. We describe several variants of the family, and show its
usefulness on several datasets.
