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🏛️國立陽明交通大學機構典藏

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    Latent Dirichlet mixture model
    (2018-02-22)
    Chien, Jen-Tzung
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    Lee, Chao-Hsi
    ;
    Tan, Zheng-Hua
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    電機工程學系
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    Department of Electrical and Computer Engineering
    Text representation based on latent topic model is seen as a non-Gaussian problem where the observed words and latent topics are multinomial variables and the topic proportionals are Dirichlet variables. Traditional topic model is established by introducing a single Dirichlet prior to characterize the topic proportionals. The words in a text document are represented by a random mixture of semantic topics. However, in real world, a single Dirichlet distribution may not faithfully reflect the variations of topic proportionals estimated from the heterogeneous documents. To address these variations, we propose a new latent variable model where latent topics and their proportionals are learned by incorporating the prior based on Dirichlet mixture model. The resulting latent Dirichlet mixture model (LDMM) is constructed for topic clustering as well as document clustering. Multiple Dirichlets provide a solution to build structural latent variables in learning representation over a variety of topics. This study carries out the inference for LDMM according to the variational Bayes and the collapsed variational Bayes. Such an unsupervised LDMM is further extended to a supervised LDMM for text classification. Experiments on document representation, summarization and classification show the merit of structural prior in LDMM topic models. (C) 2017 Elsevier B. V. All rights reserved.
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    Recent advances in machine learning for non-Gaussian data processing
    (2018-02-22)
    Ma, Zhanyu
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    Chien, Jen-Tzung
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    Tan, Zheng-Hua
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    Song, Yi-Zhe
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    Taghia, Jalil
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    DIRICHLET MIXTURE ALLOCATION
    (2016)
    Chien, Jen-Tzung
    ;
    Lee, Chao-Hsi
    ;
    Tan, Zheng-Hua
    ;
    電機工程學系
    ;
    Department of Electrical and Computer Engineering
    The topic model based on latent Dirichlet allocation relies on the prior statistics of topic proportionals for multinomial words. The words in a document are modeled as a random mixture of latent topics which are drawn from a single Dirichlet prior. However, a single Dirichlet distribution may not sufficiently characterize the variations of topic proportionals estimated from the heterogeneous documents. To deal with this concern, we present a Dirichlet mixture allocation (DMA) model which learns latent topics and their proportionals for topic and document clustering by using the prior based on a Dirichlet mixture model. Multiple Dirichlets pave a way to capture the structure of latent variables in learning representation from real-world documents covering a variety of topics. This paper builds a new latent variable model and develops a variational Bayesian inference procedure to learn model parameters consisting of mixture weights, Dirichlet parameters and word multinomials. Experiments on document representation show the merit of the proposed structural learning by increasing the number of Dirichlets in a DMA topic model.