O-IPCAC and its Application to EEG Classification
Alessandro Rozza, Gabriele Lombardi, Marco Rosa and Elena Casiraghi; JMLR W&CP 11:4-11, 2010.
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, this approach obtains promising classification performance even when the cardinality of the training set is comparable to the data dimensionality.
We demonstrate the efficacy of our algorithm by testing it on EEG data. This classification problem is particularly hard since the data are high dimensional, the cardinality of the data is lower than the space dimensionality, and the classes are strongly unbalanced. The promising results obtained in the MLSP competition, without employing any feature extraction/selection step, have demonstrated that our method is effective; this is further proved both by our tests and by the comparison with other well-known classifiers.