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Ligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Automation, Tsinghua University, Beijing 100084, China IBM Research China, Beijing, China jw-zhang06 mails,zcs mail .tsinghua.edu.cn; yqsong,liusx cn.ibm.com ABSTRACT Mining cluster evolution from multiple correlated time-varying text corpora is important in exploratory text analytics. In this paper, we propose an approach called evolutionary hierarchical D.
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The main difference is that, much like in pLSA, the matrix coefficients have a statistical interpretation. Pros: It provides better performances than LSA and pLSA. Unlike pLSA, LDA can assign a probability to a new document thanks to the document-topic Dirichlet distribution.
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In statistics and machine learning, the hierarchical Dirichlet process (HDP) is a nonparametric Bayesian approach to clustering grouped data. It uses a Dirichlet process for each group of data, with the Dirichlet processes for all groups sharing a base distribution which is itself drawn from a Dirichlet process.
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The Dirichlet Process is a stochastic process used in Bayesian nonparametrics to cluster data without specifying the number of clusters in advance. The Dirichlet Process Prior is used to mathematically descirbe prior information about the number of clusters in the data.
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Hierarchical Dirichlet process (HDP) is a powerful mixed-membership model for the unsupervised analysis of grouped data. Unlike its finite counterpart, latent Dirichlet allocation, the HDP topic model infers the number of topics from the data.
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Ultimately, LSA attempts to discover the underlying relationships between words, whereas LDA seeks to discover the underlying topics in a corpus of text. Although they both are techniques used to create a vector representation of text, they make different underlying assumptions.
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In statistics and machine learning, the hierarchical Dirichlet process (HDP) is a nonparametric Bayesian approach to clustering grouped data. It uses a Dirichlet process for each group of data, with the Dirichlet processes for all groups sharing a base distribution which is itself drawn from a Dirichlet process.
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Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Process (HDP) are both topic modeling processes. The major difference is LDA requires the specification of the number of topics, and HDP doesn't.
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The hierarchical Dirichlet Process is one of the topic modeling techniques like LDA. The major difference is that LDA requires the specification of the number of topics, and HDP doesn't. HDP has the advantage that the maximum number of topics can be unbounded and learned from the data rather than specified in advance.
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