Convex Optimization
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Subset selection for vector autoregressive processes using Lasso
Computational Statistics & Data Analysis
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This paper introduces a signal classification framework that can be used for brain-computer interface design. The actual classification is performed on sparse autoregressive features. It can use any well-known classification algorithm, such as discriminant analysis, linear logistic regression and support vector machines. The autoregressive coefficients of all signals and channels are simultaneously estimated by the group lasso, and the estimation is guided by the classification performance. Thanks to the variable selection capability of the group lasso, the framework can drop individual autoregressive coefficients that are useless in the prediction stage. Also, the framework is relatively insensitive to the chosen autoregressive order. We devise an efficient algorithm to solve this problem. We test our approach on Keirn and Aunon's data, used for binary classification of electroencephalogram signals, achieving promising results.