Principal Component Analysis

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Presentation transcript:

Principal Component Analysis Features extraction and representation By: Akanksha Sachdeva

Principal Component Analysis Abstract : In the present big data era, there is a need to process large amounts of unlabeled data and find some patterns in the data to use it further. Need to discard features that are unimportant and discover only the representations that are needed. It is possible to convert high-dimensional data to low-dimensional data using different techniques, this dimension reduction is important and makes tasks such as classification, visualization, communication and storage much easier. The loss of information should be less while mapping data from high- dimensional space to low-dimensional space.

52 Data Sets Resulting Accuracy Scores

Average Accuracy Score For SVM After PCA 0.7553846153 0.7028846153

THANK YOU