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Tensors (also called multiway arrays) are a generalization of vectors and matrices to higher dimensions based on multilinear algebra. The development of theory and algorithms for tensor decompositions (factorizations) has been an active area of study within the past decade, e.g., [1] and [2]. These methods have been successfully applied to many problems in unsupervised learning and exploratory data...
Next-generation, high-throughput sequencers are now capable of producing hundreds of billions of short sequences (reads) in a single day. The task of accurately mapping the reads back to a reference genome is of particular importance because it is used in several other biological applications, e.g., genome re-sequencing, DNA methylation, and ChiP sequencing. On a personal computer (PC), the computationally...
The common spatial patterns (CSP) algorithm has been widely used in EEG classification and brain computer interface (BCI). In this paper, we propose a multilinear formulation of the CSP, termed as TensorCSP or common tensor discriminant analysis (CTDA) for high-order tensor data. As a natural extension of CSP, the proposed algorithm uses the analogous optimization criteria in CSP and a new framework...
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