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Poster
Uncorrelation and Evenness: a New Diversity-Promoting Regularizer
Pengtao Xie · Aarti Singh · Eric Xing

Tue Aug 08 01:30 AM -- 05:00 AM (PDT) @ Gallery #133
in Posters Tue »

Latent space models (LSMs) provide a principled and effective way to extract hidden patterns from observed data. To cope with two challenges in LSMs: (1) how to capture infrequent patterns when pattern frequency is imbalanced and (2) how to reduce model size without sacrificing their expressiveness, several studies have been proposed to "diversify" LSMs, which design regularizers to encourage the components therein to be "diverse". In light of the limitations of existing approaches, we design a new diversity-promoting regularizer by considering two factors: uncorrelation and evenness, which encourage the components to be uncorrelated and to play equally important roles in modeling data. Formally, this amounts to encouraging the covariance matrix of the components to have more uniform eigenvalues. We apply the regularizer to two LSMs and develop an efficient optimization algorithm. Experiments on healthcare, image and text data demonstrate the effectiveness of the regularizer.

Author Information

Pengtao Xie (Carnegie Mellon University)
Aarti Singh (Carnegie Mellon University)
Eric Xing (Carnegie Mellon University)

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