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Poster
Spectral Methods for Supervised Topic Models
Yining Wang · Jun Zhu

Wed Dec 10 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D #None
in Poster Session »

Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference methods are based on either variational approximation or Monte Carlo sampling. This paper presents a novel spectral decomposition algorithm to recover the parameters of supervised latent Dirichlet allocation (sLDA) models. The Spectral-sLDA algorithm is provably correct and computationally efficient. We prove a sample complexity bound and subsequently derive a sufficient condition for the identifiability of sLDA. Thorough experiments on a diverse range of synthetic and real-world datasets verify the theory and demonstrate the practical effectiveness of the algorithm.

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Author Information

Yining Wang (Carnegie Mellon University)
Jun Zhu (Tsinghua University)

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