Tensor Decomposition Meets RKHS: Efficient Algorithms for Smooth and Misaligned Data

2024/08/11 by Brett W. Larsen, Tamara G. Kolda, Larsen, Brett W. +5 · 3 citations
Mathematics · Computer Science · #Tensor decomposition and applications #Computational Physics and Python Applications

paper · pdf · doi:10.48550/arxiv.2408.05677

Abstract

The canonical polyadic (CP) tensor decomposition decomposes a multidimensional data array into a sum of outer products of finite-dimensional vectors. Instead, we can replace some or all of the vectors with continuous functions (infinite-dimensional vectors) from a reproducing kernel Hilbert space (RKHS). We refer to tensors with some infinite-dimensional modes as quasitensors, and the approach of decomposing a tensor with some continuous RKHS modes is referred to as CP-HiFi (hybrid infinite and finite dimensional) tensor decomposition. An advantage of CP-HiFi is that it can enforce smoothness in the infinite dimensional modes. Further, CP-HiFi does not require the observed data to lie on a regular and finite rectangular grid and naturally incorporates misaligned data. We detail the methodology and illustrate it on a synthetic example.

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