IEEE Trans Image Process - Incremental N-mode SVD for large-scale multilinear generative models.

Tópicos

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Resumo

Tensor decomposition is frequently used in image processing and machine learning for its ability to express higher order characteristics of data. Among tensor decomposition methods, N-mode singular value decomposition (SVD) is widely used owing to its simplicity. However, the data dimension often becomes too large to perform N-mode SVD directly due to memory limitation. An incremental method to N-mode SVD can be used to resolve this issue, but existing approaches only provide a result, which is just enough to solve discriminative problems, not the full factorization result. In this paper, we present a complete derivation of the incremental N-mode SVD, which can be applied to generative models, accompanied by a technique that can reduce the computational cost by reordering calculations. The proposed incremental N-mode SVD can also be used effectively to update the current result of N-mode SVD when new training data is received. The proposed method provides a very good approximation of N -mode SVD for the experimental data, and requires much less computation in updating a multilinear model.

Resumo Limpo

tensor decomposit frequent use imag process machin learn abil express higher order characterist data among tensor decomposit method nmode singular valu decomposit svd wide use owe simplic howev data dimens often becom larg perform nmode svd direct due memori limit increment method nmode svd can use resolv issu exist approach provid result just enough solv discrimin problem full factor result paper present complet deriv increment nmode svd can appli generat model accompani techniqu can reduc comput cost reorder calcul propos increment nmode svd can also use effect updat current result nmode svd new train data receiv propos method provid good approxim n mode svd experiment data requir much less comput updat multilinear model

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