Open Access   Article Go Back

A Review on Various Matrix Factorizaton Techniques

R. Mishra1 , S. Choudhary2

Section:Review Paper, Product Type: Journal Paper
Volume-07 , Issue-10 , Page no. 13-15, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7si10.1315

Online published on May 05, 2019

Copyright © R. Mishra, S. Choudhary . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: R. Mishra, S. Choudhary, “A Review on Various Matrix Factorizaton Techniques,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.13-15, 2019.

MLA Style Citation: R. Mishra, S. Choudhary "A Review on Various Matrix Factorizaton Techniques." International Journal of Computer Sciences and Engineering 07.10 (2019): 13-15.

APA Style Citation: R. Mishra, S. Choudhary, (2019). A Review on Various Matrix Factorizaton Techniques. International Journal of Computer Sciences and Engineering, 07(10), 13-15.

BibTex Style Citation:
@article{Mishra_2019,
author = {R. Mishra, S. Choudhary},
title = {A Review on Various Matrix Factorizaton Techniques},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {10},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {13-15},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=965},
doi = {https://doi.org/10.26438/ijcse/v7i10.1315}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i10.1315}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=965
TI - A Review on Various Matrix Factorizaton Techniques
T2 - International Journal of Computer Sciences and Engineering
AU - R. Mishra, S. Choudhary
PY - 2019
DA - 2019/05/05
PB - IJCSE, Indore, INDIA
SP - 13-15
IS - 10
VL - 07
SN - 2347-2693
ER -

           

Abstract

In this work, we give the related work of fundamental matrix decomposition techniques. The primary strategy that we talk about is known as Eigen value decomposition, which breaks down the underlying matrix into an authoritative shape. The second strategy is nonnegative matrix factorization (NMF), which factorizes the underlying grid into two littler matrixes with the imperative that every component of the factorized matrix ought to be nonnegative. The third strategy is singular value decomposition (SVD) that utilizations particular estimations of the underlying network to factorize it. The last technique is CUR decomposition, which faces the issue of high thickness in factorized matrixes (an issue that is confronted when utilizing the SVD strategy). This work concludes with a description of other state-of-the-art matrix decomposition techniques

Key-Words / Index Term

Matrix Factorization, Non Negative Matrix Factorization, Singular Value Decomposition

References

[1] Pudil, P., Novoviˇ cová, J.: Novel methods for feature subset selection with respect to problem knowledge. In: Feature Extraction. Construction and Selection. Springer International Series in Engineering and Computer Science, vol. 453, pp. 101–116. Springer, US (1998).
[2] Anand Rajaraman and Jeffrey David Ullman: Mining of Massive Datasets. Cambridge University Press, New York, NY, USA (2011).
[3] Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000).
[4] Bensmail, H., Celeux, G.: Regularized gaussian discriminant analysis through eigenvalue decomposition. J. Am. Stat. Assoc. 91(436), 1743–1748 (1996).
[5] Drineas, P., Kannan, R., Mahoney, M.W.: Fast monte carlo algorithms for matrices III: computing a compressed approximate matrix decomposition. SIAM J. Comput. 36(1), 184–206 (2006).
[6] Mahoney, M.W., Maggioni, M., Drineas, P.: Tensor-cur decompositions for tensor-based data. SIAM J. Matrix Anal. Appl. 30(3), 957–987 (2008).
[7] Anand Rajaraman and Jeffrey David Ullman: Mining of Massive Datasets. Cambridge University Press, New York, NY, USA (2011).