Abstract: This work develops theories and computational methods for overcomplete, non-orthogonal tensor decomposition using convex optimization.

Feb 27, 2016 · This work develops theories and computational methods for guaranteed overcomplete, non-orthogonal tensor decomposition using convex optimization ...

Abstract—This work develops theories and computational methods for overcomplete, non-orthogonal tensor decomposition using convex optimization.

Under an incoherence condition of the rank-one factors, it is shown that one can retrieve tensor decomposition by solving a convex, infinite-dimensional ...

Feb 27, 2016 · Abstract. Tensors provide natural representations for massive multi-mode datasets and tensor methods also form the backbone of many machine ...

This set of Matlab codes reproduce the experimental results published in our paper: Overcomplete Tensor Decomposition via Convex Optimization.

This work develops theories and computational methods for guaranteed overcomplete, non-orthogonal tensor decomposition using convex optimization. We consider ...

Fingerprint. Dive into the research topics of 'Overcomplete tensor decomposition via convex optimization'. Together they form a unique fingerprint.

We propose a new algorithm for tensor decomposition, based on the simultaneous diagonaliza- tion algorithm, and apply our new algorithmic ideas to blind ...

Reduction to CP decomposition: can be efficient solved via tensor power method. Sparse Tucker Decomposition: Unmixing via Convex Optimization. Un-mix Φ from ...