Imperial College London
Alternate

STEFANOS ZAFEIRIOU, PhD

Faculty of Engineering, Department of Computing

Professor in Machine Learning & Computer Vision
 
 
 
//

Contact

 

+44 (0)20 7594 8461s.zafeiriou Website CV

 
 
//

Location

 

375Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Bahri:2019:10.1109/TPAMI.2018.2881476,
author = {Bahri, M and Panagakis, Y and Zafeiriou, SP},
doi = {10.1109/TPAMI.2018.2881476},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
pages = {2365--2379},
title = {Robust Kronecker component analysis},
url = {http://dx.doi.org/10.1109/TPAMI.2018.2881476},
volume = {41},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Dictionary learning and component analysis models are fundamental for learning compact representations relevant to a given task. The model complexity is encoded by means of structure, such as sparsity, low-rankness, or nonnegativity. Unfortunately, approaches like K-SVD that learn dictionaries for sparse coding via Singular Value Decomposition (SVD) are hard to scale, and fragile in the presence of outliers. Conversely, robust component analysis methods such as the Robust Principal Component Analysis (RPCA) are able to recover low-complexity representations from data corrupted with noise of unknown magnitude and support, but do not provide a dictionary that respects the structure of the data, and also involve expensive computations. In this paper, we propose a novel Kronecker-decomposable component analysis model, coined as Robust Kronecker Component Analysis (RKCA), that combines ideas from sparse dictionary learning and robust component analysis. RKCA has several appealing properties, including robustness to gross corruption; it can be used for low-rank modeling, and leverages separability to solve significantly smaller problems. We design an efficient learning algorithm by drawing links with tensor factorizations, and analyze its optimality and low-rankness properties. The effectiveness of the proposed approach is demonstrated on real-world applications, namely background subtraction and image denoising and completion, by performing a thorough comparison with the current state of the art.
AU  - Bahri,M
AU  - Panagakis,Y
AU  - Zafeiriou,SP
DO  - 10.1109/TPAMI.2018.2881476
EP  - 2379
PY  - 2019///
SN  - 0162-8828
SP  - 2365
TI  - Robust Kronecker component analysis
T2  - IEEE Transactions on Pattern Analysis and Machine Intelligence
UR  - http://dx.doi.org/10.1109/TPAMI.2018.2881476
UR  - https://ieeexplore.ieee.org/document/8536486
UR  - http://hdl.handle.net/10044/1/69753
VL  - 41
ER  -
Download