Bayesian Tensor Train Decomposition for Streaming Data Recovery
The author introduces the SPTT algorithm, a Bayesian tensor train decomposition method, to recover streaming data efficiently by estimating the posterior of TT format.
Singular Value Decompositions of Third-Order Reduced Biquaternion Tensors in Color Video Processing
The author introduces the applications of third-order reduced biquaternion tensors in color video processing, focusing on the singular value decomposition (SVD) and Moore-Penrose inverse. The approach aims to compress color videos efficiently using these tensor operations.
Bayesian Tensor Train Decomposition for Streaming Data Recovery
Proposing a Bayesian tensor train decomposition method, SPTT, for accurate streaming data recovery.
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data Analysis
Generalizing Tucker decomposition to continuous-indexed tensor data using Functional Bayesian Tucker Decomposition.
Chebyshev HOPGD with Sparse Grid Sampling for Parameterized Linear Systems
해당 논문은 텐서 분해를 사용하여 매개변수화된 선형 시스템의 근사 솔루션을 구축하는 Chebyshev HOPGD 방법을 제시합니다.
EinDecomp: A System for Automatic Parallel Execution of Tensor Computations
EinDecomp is a novel system that leverages an extended Einstein summation notation (EinSum) and a tensor-relational algebra (TRA) to automatically decompose and parallelize complex tensor computations for efficient execution on multi-device systems.
Statistical Inference for Low-Rank Tensors Using HOOI Algorithm: Handling Heteroskedasticity and Subgaussian Noise
This research paper presents a novel approach to performing statistically sound inference on low-rank tensors corrupted by heteroskedastic subgaussian noise, focusing on the Higher-Order Orthogonal Iteration (HOOI) algorithm for tensor singular value decomposition.
Tensor Decomposition for Unaligned Data with Applications to Longitudinal Multivariate Analysis
This paper introduces a novel tensor decomposition framework specifically designed to handle unaligned observations, a common challenge in longitudinal multivariate data analysis, by leveraging reproducing kernel Hilbert spaces (RKHS) and proposing efficient computational methods like sketching and stochastic gradient descent.
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