Svdadaptive Algorithm For Linear Least Square egressionandanomaly Reduction
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2018, Vol 20, Issue 5
Abstract
In data analysis, the accuracy of analysisdependson (1) the data representation, (2) the algorithm and (3) the metric used to measure error. For ordinary linear least square approximation (OLA), the existing formulation measures error along y or vertical direction. Conventionally, ordinary linear least square approximation (OLA) technique has been considered as the best fit regression line for linear trend data. Based on domain knowledge, several versions of OLA have been developed. They are all reformulations of OLA using prior domain knowledge, for supervised learning. Singular Value Decomposition (SVD) is also used least square approximation. The robustness of SVD approximation is attributed to (1) the SVD line is sensitive to temporal variation in time variables whereas OLA is not, it makes OLA less suitable for time sensitive data, and (2) SVD has smaller approximation error than OLA regression line. But SVD has inherent weaknesses. Herein we present a hybrid algorithm that achieves a balance between quantitative and qualitative approximation accuracy of both OLA and SVD. This algorithm is also suitable for noise reduction. Visualization is a preferred way to ascertain the quality of a new algorithm, we use MATLAB R2017b and linear regression in simple two dimensional space to demonstrate the hybrid algorithm.
Authors and Affiliations
Chaman Lal Sabharwal
Keywords
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- DOI 10.9790/0661-2005033348.
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