Scalable Sprase Bayesian Nonparametric and Matrix Tri-factorization Models for Text Mining Applications

Scalable Sprase Bayesian Nonparametric and Matrix Tri-factorization Models for Text Mining Applications

Hierarchical Bayesian Models and Matrix factorization methods provide an unsupervised way to learn latent components of data from the grouped or sequence data. For example, in document data, latent component corn-responds to topic with each topic as a distribution over a note vocabulary of words. Fo...

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Bibliographic Details
Main Author: Ranganath, B N
Other Authors: Bhatnagar, Shalabh
Language:en_US
Published: 2018
Subjects:
Scalable Sprase Bayesian
Matrix Tri-factorization Model
Mining Application - Texts
Hierarchical Bayesian Models
Sparse Entity Resolution Model (SERM)
Sparse Topical Analysis
Scalable Focussed Entity Resolution
Block Exchangeable Model (BEM)
Sequential Grouped Data
Dirichlet Process
Computer Science
Online Access:http://etd.iisc.ernet.in/2005/3593
http://etd.iisc.ernet.in/abstracts/4462/G28208-Abs.pdf

Internet

http://etd.iisc.ernet.in/2005/3593
http://etd.iisc.ernet.in/abstracts/4462/G28208-Abs.pdf

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