Next Article in Journal
Estimating Cost Savings from Early Cancer Diagnosis
Previous Article in Journal
Development of a Data Set of Pesticide Dissipation Rates in/on Various Plant Matrices for the Pesticide Properties Database (PPDB)
Article Menu

Export Article

Open AccessArticle

Adjustable Robust Singular Value Decomposition: Design, Analysis and Application to Finance

Institute for Financial Services Analytics, University of Delaware, Newark, DE 19716, USA
Received: 11 August 2017 / Revised: 22 August 2017 / Accepted: 27 August 2017 / Published: 30 August 2017
View Full-Text   |   Download PDF [501 KB, uploaded 31 August 2017]   |  

Abstract

The Singular Value Decomposition (SVD) is a fundamental algorithm used to understand the structure of data by providing insight into the relationship between the row and column factors. SVD aims to approximate a rectangular data matrix, given some rank restriction, especially lower rank approximation. In practical data analysis, however, outliers and missing values maybe exist that restrict the performance of SVD, because SVD is a least squares method that is sensitive to errors in the data matrix. This paper proposes a robust SVD algorithm by applying an adjustable robust estimator. Through adjusting the tuning parameter in the algorithm, the method can be both robust and efficient. Moreover, a sequential robust SVD algorithm is proposed in order to decrease the computation volume in sequential and streaming data. The advantages of the proposed algorithms are proved with a financial application. View Full-Text
Keywords: Singular Value Decomposition (SVD); robustness; sequential data analysis; financial application Singular Value Decomposition (SVD); robustness; sequential data analysis; financial application
Figures

Figure 1

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. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wang, D. Adjustable Robust Singular Value Decomposition: Design, Analysis and Application to Finance. Data 2017, 2, 29.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Data EISSN 2306-5729 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top