Randomized algorithms for computation of Tucker decomposition and higher order SVD (HOSVD)
Big data analysis has become a crucial part of new emerging technologies such as the
internet of things, cyber-physical analysis, deep learning, anomaly detection, etc. Among …
internet of things, cyber-physical analysis, deep learning, anomaly detection, etc. Among …
L1-norm Tucker tensor decomposition
DG Chachlakis, A Prater-Bennette… - IEEE Access, 2019 - ieeexplore.ieee.org
Tucker decomposition is a standard multi-way generalization of Principal-Component
Analysis (PCA), appropriate for processing tensor data. Similar to PCA, Tucker …
Analysis (PCA), appropriate for processing tensor data. Similar to PCA, Tucker …
Mars: Masked automatic ranks selection in tensor decompositions
M Kodryan, D Kropotov… - … Conference on Artificial …, 2023 - proceedings.mlr.press
Tensor decomposition methods have proven effective in various applications, including
compression and acceleration of neural networks. At the same time, the problem of …
compression and acceleration of neural networks. At the same time, the problem of …
Tensor methods for generating compact uncertainty quantification and deep learning models
Tensor methods have become a promising tool to solve high-dimensional problems in the
big data era. By exploiting possible low-rank tensor factorization, many high-dimensional …
big data era. By exploiting possible low-rank tensor factorization, many high-dimensional …
[图书][B] Theory and Algorithms for Reliable Multimodal Data Analysis, Machine Learning, and Signal Processing
DG Chachlakis - 2021 - search.proquest.com
Modern engineering systems collect large volumes of data measurements across diverse
sensing modalities. These measurements can naturally be arranged in higher-order arrays …
sensing modalities. These measurements can naturally be arranged in higher-order arrays …