Abstract
In this paper,we introduce a new topic model named Gaussian-LDA, which is more suitable to model continuous data. Topic Model based on latent Dirichlet allocation (LDA) is widely used for the statistical analysis of document collections and other discrete data. The LDA model assumes that the words of each document arise from a mixture of topics, each of which is a multinomial distribution over the vocabulary. To apply the original LDA to process continuous data, discretization based vector quantization must be done beforehand, which usually results in information loss. In the proposed model, we consider continuous emission probability, Gaussian instead of multinomial distribution. This new topic model demonstrates higher performance than standard LDA in the experiments of audio retrieval.
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© 2012 Springer-Verlag Berlin Heidelberg
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Hu, P., Liu, W., Jiang, W., Yang, Z. (2012). Latent Topic Model Based on Gaussian-LDA for Audio Retrieval. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_68
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DOI: https://doi.org/10.1007/978-3-642-33506-8_68
Publisher Name: Springer, Berlin, Heidelberg
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