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Incremental Learning of Independent Topic Analysis
Abstract:In this paper, we present a method of applying Independent Topic Analysis (ITA) to increasing the number of document data. The number of document data has been increasing since the spread of the Internet. ITA was presented as one method to analyze the document data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis (ICA). ICA is a technique in the signal processing; however, it is difficult to apply the ITA to increasing number of document data. Because ITA must use the all document data so temporal and spatial cost is very high. Therefore, we present Incremental ITA which extracts the independent topics from increasing number of document data. Incremental ITA is a method of updating the independent topics when the document data is added after extracted the independent topics from a just previous the data. In addition, Incremental ITA updates the independent topics when the document data is added. And we show the result applied Incremental ITA to benchmark datasets.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128933Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 598
 Akhtar, M. T., Jung, T.-P., Makeig, S., and Cauwenberghs, G., 2012. Recursive independent component analysis for online blind source separation, IEEE International Symposium on Circuits and Systems.
 Amari, S., Cichocki, A., and Yang, H.-H., 1996. A new learning algorithm for blind signal separation, In Advances in Neural Information Processing Systems, D.S. Touretzky, M.C. Mozer, and M.E. Hasselmo, Editor, Vol.8, pp.757–763, The MIT Press.
 Azoury, K. S., and Warmuth, M. K., 2001. Relative loss bounds for on-line density estimation with the exponential family of distribution, Machine Learning, Vol.42, No.3, pp.211–246.
 Banerjee, A., Merugu, S., Dhillon, I., and Ghosh, J., 2005. Clustering with Bregman divergences, Journal of Machine Learning Research, Vol. 6, pp.1705–1749.
 Banerjee, A., and Basu, S. 2007. Topic Models over Text Streams: A Study of Batch and Online Unsupervised Learning, SIAM International Conference on Data Mining.
 Bassiou, N. K., and Kotropoulos, C. L., 2014. Online PLSA: Batch Updating Techniques Including Out-of-Vocabulary Words, IEEE Transaction on Neural Networks and Learning Systems, Vol.25, Issue.11, pp.1953–1966.
 Bell, A. J., and Sejnowski, T. J., 1997. An information-maximization approach to blind separation and blind dexonvolution, Neural Computation, Vol.7, pp.1129–1159.
 Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent dirichlet allocation, The Journal of Machine Learning Research, Vol. 3, pp. 993–1022.
 Blei, D. M. 2012. Probabilistic topic models, Commun. ACM, Vol. 55, No. 4, pp. 77–84.
 Brown, G., Pocock, A., Zhao, M-J., and Luj´an, M. 2012. Conditional likelihood maximisation: A unifying framework for information theoretic feature, Journal of Machine Learning Research (JMLR), Vol. 13, pp. 27–66.
 Chien, J.-T., and Wu, M.-S., 2007. Adaptive Bayesian Latent Semantic Analysis, IEEE Transactions on Audio, Speech and Language Processing, Vol.16, Issue.1, pp.198–207.
 Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. 1990. Indexing by latent semantic analysis, Journal of the American Society of Information Science, Vol. 41, No. 6, pp. 391–407.
 Elkan, C., 2006. Clustering documents with an exponential-family approximation of the Dirichlet compund multinomial distribution, In Proceedings of the 23rd International Conference on Machine Learning.
 Hertz, J., Krogh, A., and Palmer, R. G., 1991. Introduction To the Theory of Neural Computation, Addison-Wesley, Reading, MA.
 Hofmann, T. 1999. Probabilistic latent semantic analysis, Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI’99), pp. 289–29, Morgan Kaufmann Publishers Inc..
 Hyv¨arinen A. 1999. Fast and robust fixed-point algorithms for independent component analysis, IEEE Trans. on Neural Networks, Vol. 10, No. 3.
 Hyv¨arinen, A., Karhunen, J. and Oja, E. 2001. Independent component analysis, John Wiley & Sons.
 Lichman, M. 2013. UCI machine learning repository, http://archive.ics.uci.edu/ml , Accessed on 11/11/2016.
 Madsen, R., Kauchak, D., and Elkan, C., 2005. Modeling word burstiness using the Dirichlet distribution, In Proceedings of the 22nd International Conference on Machine Learning.
 Neal, R.M., and Hinton, G.E., 1998. A view of the EM algorithm that justifies incremental, sparse, and other variants. In M.I. Jordan, editor, Learning in Graphical Model, pp.355–368, MIT Press.
 Oja, E., and Karhunen, J., 1985. On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix, Journal of Mathematical Analysis and Application, Vol.106, pp.69–84.
 Salton, G., Fox, E. A., Wu, H. 1983. Extended boolean information retrieval, Commun. ACM, Vol. 26, No. 11, pp. 1022–1036.
 Sanger, T. D., 1989. Optimal unsupervised learning in a single-layer linear feedforward neural network, IEEE Transaction on Neural Networks, Vol.2, pp. 459–473.
 Schraudolph, N. N., and Giannakopoulos, X., 1999. Online Independent Component Analysis With Local Learning Rate Adaptation, NIPS, pp. 789–795
 Shinohara, Y. 1999. Independent Topic Analysis : Extraction of Characteristic Topics by maximization of Independence, Technical report of IEICE.
 Shinohara, Y. 2000. Development of Browsing Assistance System for finding Primary Topics and Tracking their Changes in a Document Database, CRIEPI Research Report.
 Sirovich, I., and Kirby, M., 1987. Low-Dimensional procedure for the caracterization of human faces, Journal of Optical Society of America A, Vol.4, No.3, pp.519–524.
 Song, X., Lin, C.-Y., Tseng, B. L., Sun, M.-T., 2005. Modeling and predicting personal information dissemination behavior, In Proceedings of the 11th ACM SIGKKD International Conference on Knowledge Discovery and Data Mining.
 Tanaka, M, Shinohara, Y. 2003. Topic-Based Dynamic Document Management System for discovering Important and New Topics, CRIEPI Research Report.
 Weng, J., Zhang, Y, and Hwang, W.-S., 2003. Candid Covariance-free Incremental Principal Compoenent Aalysis, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.25, No.8, pp.1034–1040.
 Zhao, Y. and Karypis, G. 2002. Evaluation of hierarchical clustering algorithms for document datasets, Conference of Information and Knowledge Management (CIKM), pp. 515–524, ACM.
 Zhong, S., and Ghosh, J. 2003. A comparative study of generative models for document clustering, Data Mining Workshop on Clustering High Dimensional Data and Its Applications.