Abstract
Understanding the relevance of extracted features in domain-specific sense is a matter at the heart of image classification. In this paper, we propose a feature selection framework that allows more compactness of the statistical model while holding good generalization to unseen data. Both feature selection and clustering are based on well-established statistical models that provide natural choice when the data to model are spherical. Moreover, we develop a probabilistic kernel based on Fisher score and mixture of von Mises model (moVM) to feed Support Vector Machines (SVM). The selection process evaluates the relevance of features through a principled feature saliency approach. The unsupervised learning is approached using Expectation Maximization (EM) for parameter estimation along with Minimum Message Length (MML) to determine the optimal number of mixture components. We argue that the proposed framework is well-justified and can be adjusted to different problems. Experimental results involving the challenging problem of image-based spam filtering show the merits of the proposed approach.
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Amayri, O., Bouguila, N. (2012). Unsupervised Feature Selection for Spherical Data Modeling: Application to Image-Based Spam Filtering. In: Dziech, A., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2012. Communications in Computer and Information Science, vol 287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30721-8_2
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DOI: https://doi.org/10.1007/978-3-642-30721-8_2
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