Tensor principal component analysis
Tensor principal component analysis (PCA) has attracted increasing attention recently because of its effectiveness in multiway or tensor data analysis. This chapter introduces tensor PCA [1], [2] and its variants, including robust tensor PCA (R-TPCA) [2], tensor low-rank representation (TLRR) [3], a...
Saved in:
Main Authors: | ZHOU, Pan, LU, Canyi, LIN, Zhouchen |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2022
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/9050 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Tensor factorization for low-rank tensor completion
by: ZHOU, Pan, et al.
Published: (2017) -
Tensor low-rank representation for data recovery and clustering
by: ZHOU, Pan, et al.
Published: (2021) -
A fast correction approach to tensor robust principal component analysis
by: Zhang, Zhechen, et al.
Published: (2024) -
Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria
by: TAO, Dacheng, et al.
Published: (2007) -
Interpretable tensor fusion
by: VARSHNEYA, Saurabh, et al.
Published: (2024)