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Keywords: Fisher subspace Supervised learning Discriminant techniques Small sample size problem a b s t r a c t At the present, several applications need to classify high dimensional points belonging to highly unbalanced classes. Unfortunately, when the training set cardinality is small compared to the data dimensionality (''small sample size'' problem) the(More)
In the last decades the estimation of the intrinsic dimen-sionality of a dataset has gained considerable importance. Despite the great deal of research work devoted to this task, most of the proposed solutions prove to be unreliable when the intrinsic dimensionality of the input dataset is high and the manifold where the points lie is nonlin-early embedded(More)
Twitter is one of the most popular micro-blogging services in the world, often studied in the context of political opinion mining for its peculiar nature of online public discussion platform. In our work we analyse the phenomenon of <i>political disaffection</i> defined as the "lack of confidence in the political process, politicians, and democratic(More)
Recently, a great deal of research work has been devoted to the development of algorithms to estimate the intrinsic dimensionality (id) of a given dataset, that is the minimum number of parameters needed to represent the data without information loss. id estimation is important for the following reasons: the capacity and the generalization capability of(More)
This paper proposes a novel two-class classifier, called IPCAC, based on the Isotropic Principal Component Analysis technique; it allows to deal with training data drawn from Mixture of Gaussian distributions, by projecting the data on the Fisher subspace that separates the two classes. The obtained results demonstrate that IPCAC is a promising technique;(More)
In this paper we describe an online/incremental linear binary classifier based on an interesting approach to estimate the Fisher subspace. The proposed method allows to deal with datasets having high cardinality, being dynamically supplied, and it efficiently copes with high dimensional data without employing any dimensionality reduction technique. Moreover(More)