• Yengkhom Satyendra Singh
• Benaki Lairenjam

Singular value decomposition (SVD) is an algebraic tool deals with singular matrices or that are much closed to singular ones. We know that SVD offers significant properties in science and engineering particularly in data science and signal processing. Here in this paper, we will discuss the importance of SVD and its applications. Also, we will present some of its properties that have not been utilized properly, say its properties in various image applications are at its early stage. This paper presents in using various properties of SVD and some new contributions that were originated from SVD properties analysis. Our aim is to highlight the importance of SVD and its applications in data science and point out its various features and important applications and forecast its future research directions.

Singular value decomposition; fault diagnosis; signal processing and image processing.

"Singular value decomposition and its applications ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 11, page no.180-182, November-2020, Available :http://www.jetir.org/papers/JETIR2011029.pdf

"Singular value decomposition and its applications ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 11, page no. pp180-182, November-2020, Available at : http://www.jetir.org/papers/JETIR2011029.pdf