@inproceedings{ac0563f984da4c76a30c516e6698ea8b,
title = "Performance analysis of least-squares khatri-rao factorization",
abstract = "The least-squares Khatri-Rao factorization is regarded as an important linear- and multilinear-algebraic tool and finds applications in, for instance, computation of the CP decomposition and channel estimation for two-way relaying systems. We conduct a 'first-order' perturbation analysis for it, which is a crucial step towards establishing analytical performance evaluation of various schemes employing the least-squares Khatri-Rao factorization. Numerical results validating our analytical performance analysis are shown. Being new advance in perturbation analysis on matrix decompositions, the performance analysis of the least-squares Khatri-Rao factorization presented in this paper will also contribute to a promising enhancement of the SECSI-GU framework, which is able to estimate the loading matrices in a CP decomposition, both efficiently and accurately.",
author = "Yao Cheng and Cheema, {Sher Ali} and Martin Haardt and Amir Weiss and Arie Yeredor",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 ; Conference date: 10-12-2017 Through 13-12-2017",
year = "2018",
month = mar,
day = "9",
doi = "10.1109/CAMSAP.2017.8313178",
language = "الإنجليزيّة",
series = "2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
booktitle = "2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017",
address = "الولايات المتّحدة",
}