Dimensionality Reduction Application of Big Data Based on PCA
With the rapid development of the Internet and information technology,the extensiveness and complexity of data sources have brought great challenges to obtaining information accuracy.Using a suitable dimen-sionality reduction algorithm can reduce these massive data from high dimensionality to an acceptable range without losing the meaning expressed by the original data,and the amount of calculation is greatly reduced,making it easier to understand.PCA(principal component analysis)is principal component analysis as one of the important algorithms for data dimensionality reduction.It uses orthogonal transformation to convert a set of related variables into a set of linearly uncorrelated variables.Usually,this transformation will reduce the number of variables,calculate the contribu-tion of each component in the expression data,and select the top features with the highest contribution to express the entire data set.Experiments show that the entire data set can be expressed by reducing the principal components from multi-dimensional to two-dimensional,which greatly reduces the amount of calculation within the range of controllable accuracy.
Big data dimensionality reductionDimensionality reduction visualizationArtificial intelligenceIn-telligent recommendationArtificial intelligence and intelligent manufacturing
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