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High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models
Majid Janzamin, Animashree Anandkumar
Fitting high-dimensional data involves a delicate tradeoff between faithful representation and the use of sparse models. Too often, sparsity assumptions on the fitted model are too…
Tensor decompositions for learning latent variable models
Anima Anandkumar, Rong Ge, Daniel Hsu +2
This work considers a computationally and statistically efficient parameter estimation method for a wide class of latent variable models---including Gaussian mixture models, hidden…
A Spectral Algorithm for Latent Dirichlet Allocation
Animashree Anandkumar, Dean P. Foster, Daniel Hsu +2
The problem of topic modeling can be seen as a generalization of the clustering problem, in that it posits that observations are generated due to multiple latent factors (e.g., the…
Learning loopy graphical models with latent variables: Efficient methods and guarantees
Animashree Anandkumar, Ragupathyraj Valluvan
The problem of structure estimation in graphical models with latent variables is considered. We characterize conditions for tractable graph estimation and develop efficient methods…
A Method of Moments for Mixture Models and Hidden Markov Models
Animashree Anandkumar, Daniel Hsu, Sham M. Kakade
Mixture models are a fundamental tool in applied statistics and machine learning for treating data taken from multiple subpopulations. The current practice for estimating the param…