Tensor Decomposition Meets RKHS: Efficient Algorithms for Smooth and Misaligned Data
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.
Cite
@article{arxiv.2408.05677,
title = {Tensor Decomposition Meets RKHS: Efficient Algorithms for Smooth and Misaligned Data},
author = {Brett W. Larsen and Tamara G. Kolda and Anru R. Zhang and Alex H. Williams},
journal= {arXiv preprint arXiv:2408.05677},
year = {2024}
}