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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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Improved Parsimonious Topic Modeling Based on the Bayesian Information Criterion.

Hang Wang¹, David Miller¹

¹Electrical Engineering and Computer Science Department, The Pennsylvania State University, State College, PA 16802, USA.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

This summary is machine-generated.

Enhanced Parsimonious Topic Model (PTM) improves text analysis by increasing model sparsity and optimizing parameter learning. This advanced PTM offers superior performance and a more refined topic representation compared to the original model.

Keywords:

Bayesian information criterion expectation maximization algorithm medical abstracts topic model

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Area of Science:

Natural Language Processing
Machine Learning
Computational Linguistics

Background:

Previous work introduced Parsimonious Topic Model (PTM) for text corpora, offering sparse topic representation unlike Latent Dirichlet Allocation (LDA).
PTM determines salient words per topic and relevant topics per document, using a Bayesian Information Criterion (BIC) for unsupervised model selection.
The original PTM determined topic-specific words, document-specific topics, parameters, and topic count in an unsupervised manner.

Purpose of the Study:

To propose and evaluate extensions to the Parsimonious Topic Model (PTM) for improved text corpus analysis.
To enhance model sparsity and optimize the parameter learning algorithm for more robust topic modeling.
To achieve superior performance and sparser topic representations compared to the original PTM.

Main Methods:

Modified the BIC objective function using a lossless coding scheme to penalize non-salient words, increasing model sparsity.
Developed a new parameter learning strategy that jointly optimizes word switches across topics, avoiding local optima.
Applied the extended PTM to several document datasets for performance evaluation.

Main Results:

The proposed PTM extensions resulted in increased model sparsity and enabled the selection of more topics at a lower BIC cost.
The joint optimization of word switches demonstrated improved susceptibility to finding better local minima compared to sequential updates.
The enhanced PTM outperformed the original PTM across multiple performance measures on document datasets.

Conclusions:

The developed modeling and algorithmic extensions significantly improve Parsimonious Topic Model (PTM) performance and sparsity.
The new methods provide a more robust and effective approach to unsupervised topic modeling in text corpora.
The enhanced PTM offers a sparser and more accurate representation of topics within documents.