Estimating likelihoods for topic models

Wray Buntine*

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    32 Citations (Scopus)

    Abstract

    Topic models are a discrete analogue to principle component analysis and independent component analysis that model topic at the word level within a document. They have many variants such as NMF, PLSI and LDA, and are used in many fields such as genetics, text and the web, image analysis and recommender systems. However, only recently have reasonable methods for estimating the likelihood of unseen documents, for instance to perform testing or model comparison, become available. This paper explores a number of recent methods, and improves their theory, performance, and testing.

    Original languageEnglish
    Title of host publicationAdvances in Machine Learning - First Asian Conference on Machine Learning, ACML 2009, Proceedings
    Pages51-64
    Number of pages14
    DOIs
    Publication statusPublished - 2009
    Event1st Asian Conference on Machine Learning, ACML 2009 - Nanjing, China
    Duration: 2 Nov 20094 Nov 2009

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume5828 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference1st Asian Conference on Machine Learning, ACML 2009
    Country/TerritoryChina
    CityNanjing
    Period2/11/094/11/09

    Fingerprint

    Dive into the research topics of 'Estimating likelihoods for topic models'. Together they form a unique fingerprint.

    Cite this