Singular Value Decomposition Guided Image Compression

Md.Ashraful Alam, Abdul Halim Bhuiyan, Khandker Farid Uddin Ahmed, Md.Nazmul Hasan Sakib

Abstract


This paper presents singular value decomposition (SVD), a major technique to matrix decomposition. SVD functions as the fundamental scientific instrument of numerous applications including principal component analysis (PCA), matrix approximation, Eigen Value decomposition, Cholesky decomposition and others. SVD is operated in many applications for example data analysis, Netflix’s recommender method, Google’s PageRank algorithm, image compression, and dimensional reduction while retaining the most significant information. This paper indicates the mathematics following SVD in a modest way. Furthermore, it applies SVD method in dimensionality reduction and image compression as the essential technique of the data analysis.

Keywords


Data Analysis, Singular Value Decomposition, Dimensionality reduction, Eigen Value Decomposition, Machine Learning

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References


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DOI: http://dx.doi.org/10.52155/ijpsat.v47.2.6746

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