Using Singular Value Decomposition to Reduce Dimensionality of Initial Data Set
Олег Ужга-Ребров, Galina Kuleshova
Abstract
The purpose of any data analysis is to extract essential information implicitly present in the data. To do this, it often seems necessary to transform the initial data into a form that allows one to identify and interpret the essential features of their structure. One of the most important tasks of data analysis is to reduce the dimension of the original data. The paper considers an approach to solving this problem based on singular value decomposition (SVD).
Topics & Concepts
Singular value decompositionCurse of dimensionalityDimension (graph theory)Computer scienceDecompositionData setSet (abstract data type)Data miningDimensionality reductionValue (mathematics)AlgorithmMathematicsArtificial intelligenceMachine learningEcologyBiologyPure mathematicsProgramming languageAdvanced Data Processing TechniquesCybersecurity and Information SystemsNeural Networks and Applications