Neural networks for pattern recognition
Neural networks for pattern recognition
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
The Journal of Machine Learning Research
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Brain state decoding for rapid image retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Second-Order Bilinear Discriminant Analysis
The Journal of Machine Learning Research
BLR-D: applying bilinear logistic regression to factored diagnosis problems
SLAML '11 Managing Large-scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques
BLR-D: applying bilinear logistic regression to factored diagnosis problems
ACM SIGOPS Operating Systems Review
Decoding of EEG activity from object views: active detection vs. passive visual tasks
BI'12 Proceedings of the 2012 international conference on Brain Informatics
Robust common spatial filters with a maxmin approach
Neural Computation
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Factor analysis and discriminant analysis are often used as complementary approaches to identify linear components in two dimensional data arrays. For three dimensional arrays, which may organize data in dimensions such as space, time, and trials, the opportunity arises to combine these two approaches. A new method, Bilinear Discriminant Component Analysis (BDCA), is derived and demonstrated in the context of functional brain imaging data for which it seems ideally suited. The work suggests to identify a subspace projection which optimally separates classes while ensuring that each dimension in this space captures an independent contribution to the discrimination.