Stochastic Matrix Factorization
원문 초록
This paper considers a restriction to non-negative matrix factorization in which at least one matrix factor is stochastic. That is, the elements of the matrix factors are non-negative and the columns of one matrix factor sum to 1. This restriction includes topic models, a popular method for analyzing unstructured data. It also includes a method for storing and finding pictures. The paper presents necessary and sufficient conditions on the observed data such that the factorization is unique. In addition, the paper characterizes natural bounds on the parameters for any observed data and presents a consistent least squares estimator. The results are illustrated using a topic model analysis of PhD abstracts in economics and the problem of storing and retrieving a set of pictures of faces.
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Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics
Evaluating Negative Sampling Approaches for Neural Topic Models
Seeded Poisson Factorization: Leveraging domain knowledge to fit topic models
Refining Dimensions for Improving Clustering-based Cross-lingual Topic Models
Locating the Leading Edge of Cultural Change
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