Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
An information-theoretic perspective of tf—idf measures
Information Processing and Management: an International Journal
Modeling word burstiness using the Dirichlet distribution
ICML '05 Proceedings of the 22nd international conference on Machine learning
A discrete mixture-based kernel for SVMs: Application to spam and image categorization
Information Processing and Management: an International Journal
Improving probabilistic information retrieval by modeling burstiness of words
Information Processing and Management: an International Journal
Supervised input space scaling for non-negative matrix factorization
Signal Processing
Non-linear book manifolds: learning from associations the dynamic geometry of digital libraries
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
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The Dirichlet compound multinomial (DCM) distribution has recently been shown to be a good model for documents because it captures the phenomenon of word burstiness, unlike standard models such as the multinomial distribution. This paper investigates the DCM Fisher kernel, a function for comparing documents derived from the DCM. We show that the DCM Fisher kernel has components that are similar to the term frequency (TF) and inverse document frequency (IDF) factors of the standard TF-IDF method for representing documents. Experiments show that the DCM Fisher kernel performs better than alternative kernels for nearest-neighbor document classification, but that the TF-IDF representation still performs best.