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This paper deals with the comparison of different dimensionality reduction techniques when combined with various classification techniques. The dimensionality reduction techniques considered are PCA, ICA, TSVD, LSI and RP. They are mainly used for feature extraction. Their main goal is to reduce noisy data, redundant data, memory/disk needed to store data. They prevent the problem of over-fitting and help to visualize high dimensional data. ANN, SVM, Naïve Bayes, K-NN, Random Forest are some ofdoi:10.17148/iarjset.2015.2905 fatcat:qcx3pdtv65gc7dkxzg7cdljqb4