A Linear Classification Method in a Very High Dimensional Space Using Distributed Representation

  • Authors:

  • Takao Kobayashi;Ikuko Shimizu

  • Affiliations:

  • Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Japan 184-8588;Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Japan 184-8588

  • Venue:
  • MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
  • Year:
  • 2009

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Abstract

We have proposed a fast learning and classification method by using distributed representation of vectors. In this paper, first, we shows that our method provides faster and better performance than 1-NN method by introducing a definition of a similarity concerned with LSH scheme. Next we compare our method with the Naive Bayes with respect to the number of dimensions of features. While the Naive Bayes requires a considerably large dimensional feature space, our method achieves higher performance even where the number of dimensions of a feature space of our method is much smaller than that of Naive Bayes. We explain our method by formalizing as a linear classifier in a very high dimensional space and show it is a special case of Naive Bayes model. Experimental results show that our method provides superior classification rates with small time complexity of learning and classification and is applicable to large data set.