Faster Robust Tensor Power Method for Arbitrary Order
원문 초록
Tensor decomposition is a fundamental method used in various areas to deal with high-dimensional data. \emph{Tensor power method} (TPM) is one of the widely-used techniques in the decomposition of tensors. This paper presents a novel tensor power method for decomposing arbitrary order tensors, which overcomes limitations of existing approaches that are often restricted to lower-order (less than $3$) tensors or require strong assumptions about the underlying data structure. We apply sketching method, and we are able to achieve the running time of $\widetilde{O}(n^{p-1})$, on the power $p$ and dimension $n$ tensor. We provide a detailed analysis for any $p$-th order tensor, which is never given in previous works.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Revisit CP Tensor Decomposition: Statistical Optimality and Fast Convergence
D-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams
Multiscale Tensor Summation Factorization as a New Neural Network Layer (MTS Layer) for Multidimensional Data Processing
Knowledge Graph Completion with Mixed Geometry Tensor Factorization
Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
Guaranteed Multidimensional Time Series Prediction via Deterministic Tensor Completion Theory
토론
이 논문과 연결된 포럼 글이 아직 없습니다.