Sparse Poisson coding for high dimensional document clustering
Refereed conference paper presented and published in conference proceedings

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AbstractDocument clustering plays an important role in large scale textual data analysis, which generally faces with great challenge of the high dimensional textual data. One remedy is to learn the high-level sparse representation by the sparse coding techniques. In contrast to traditional Gaussian noise-based sparse coding methods, in this paper, we employ a Poisson distribution model to represent the word-count frequency feature of a text for sparse coding. Moreover, a novel sparse-constrained Poisson regression algorithm is proposed to solve the induced optimization problem. Different from previous Poisson regression with the family of ℓ1-regularization to enhance the sparse solution, we introduce a sparsity ratio measure which make use of both ℓ1-norm and ℓ2-norm on the learned weight. An important advantage of the sparsity ratio is that it bounded in the range of 0 and 1. This makes it easy to set for practical applications. To further make the algorithm trackable for the high dimensional textual data, a projected gradient descent algorithm is proposed to solve the regression problem. Extensive experiments have been conducted to show that our proposed approach can achieve effective representation for document clustering compared with state-of-the-art regression methods. © 2013 IEEE.
All Author(s) ListChenxia Wu, Haiqin Yang, Jianke Zhu, Jiemi Zhang, Irwin King, Michael R. Lyu
Name of Conference2013 IEEE International Conference on Big Data, Big Data 2013
Start Date of Conference06/10/2013
End Date of Conference09/10/2013
Place of ConferenceSanta Clara, CA
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings of the 2013 IEEE International Conference on Big Data
Pages512 - 517
LanguagesEnglish-United Kingdom
Keywordsdocument clustering, Poisson regression, sparse coding

Last updated on 2021-14-10 at 23:34