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Tensor Analysis and Learning

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If you have a question about this talk, please contact Hector Basevi.

Host: Dr Ata Kaban

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This talk will focus on tensor-based machine learning methods for extracting useful information from multidimensional data, such as videos and fMRI sequences. The first part of the talk will be devoted to multilinear subspace learning (MSL) for dimensionality reduction, where we learn compact features directly from tensor representations of big multidimensional data. In particular, we will discuss in detail the most influential MSL method, multilinear principal component analysis (MPCA). The second part will give a broad view on our current research focuses and future directions. We will present our recent works on tensor analysis and learning with sparsity, uncertainty, and incompleteness. In particular, we will talk about our latest development in whole-brain fMRI analysis for brain state decoding and interpretation.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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