O-IPCAC and its Application to EEG Classification

Alessandro Rozza, Gabriele Lombardi, Marco Rosa, Elena Casiraghi
Proceedings of the First Workshop on Applications of Pattern Analysis, PMLR 11:4-11, 2010.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v11-rozza10a, title = {O-IPCAC and its Application to EEG Classification}, author = {Rozza, Alessandro and Lombardi, Gabriele and Rosa, Marco and Casiraghi, Elena}, booktitle = {Proceedings of the First Workshop on Applications of Pattern Analysis}, pages = {4--11}, year = {2010}, editor = {Diethe, Tom and Cristianini, Nello and Shawe-Taylor, John}, volume = {11}, series = {Proceedings of Machine Learning Research}, address = {Cumberland Lodge, Windsor, UK}, month = {01--03 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v11/rozza10a/rozza10a.pdf}, url = {https://proceedings.mlr.press/v11/rozza10a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T O-IPCAC and its Application to EEG Classification %A Alessandro Rozza %A Gabriele Lombardi %A Marco Rosa %A Elena Casiraghi %B Proceedings of the First Workshop on Applications of Pattern Analysis %C Proceedings of Machine Learning Research %D 2010 %E Tom Diethe %E Nello Cristianini %E John Shawe-Taylor %F pmlr-v11-rozza10a %I PMLR %P 4--11 %U https://proceedings.mlr.press/v11/rozza10a.html %V 11 %X 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.
RIS
TY - CPAPER TI - O-IPCAC and its Application to EEG Classification AU - Alessandro Rozza AU - Gabriele Lombardi AU - Marco Rosa AU - Elena Casiraghi BT - Proceedings of the First Workshop on Applications of Pattern Analysis DA - 2010/09/30 ED - Tom Diethe ED - Nello Cristianini ED - John Shawe-Taylor ID - pmlr-v11-rozza10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 11 SP - 4 EP - 11 L1 - http://proceedings.mlr.press/v11/rozza10a/rozza10a.pdf UR - https://proceedings.mlr.press/v11/rozza10a.html AB - 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. ER -
APA
Rozza, A., Lombardi, G., Rosa, M. & Casiraghi, E.. (2010). O-IPCAC and its Application to EEG Classification. Proceedings of the First Workshop on Applications of Pattern Analysis, in Proceedings of Machine Learning Research 11:4-11 Available from https://proceedings.mlr.press/v11/rozza10a.html.

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