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EMMCVPR
2009
Springer

Clustering-Based Construction of Hidden Markov Models for Generative Kernels

10 years 6 months ago
Clustering-Based Construction of Hidden Markov Models for Generative Kernels
Generative kernels represent theoretically grounded tools able to increase the capabilities of generative classification through a discriminative setting. Fisher Kernel is the first and mostly-used representative, which lies on a widely investigated mathematical background. The manufacture of a generative kernel flows down through a two-step serial pipeline. In the first, “generative” step, a generative model is trained, considering one model for class or a whole model for all the data; then, features or scores are extracted, which encode the contribution of each data point in the generative process. In the second, “discriminative” part, the scores are evaluated by a discriminative machine via a kernel, exploiting the data separability. In this paper we contribute to the first aspect, proposing a novel way to fit the class-data with the generative models, in specific, focusing on Hidden Markov Models (HMM). The idea is to perform model clustering on the unlabeled data in...
Manuele Bicego, Marco Cristani, Vittorio Murino, E
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where EMMCVPR
Authors Manuele Bicego, Marco Cristani, Vittorio Murino, Elzbieta Pekalska, Robert P. W. Duin
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