Random Projection in Machine Learning
In many signal classification domains such as image or video processing, the high dimensionality of the input signals makes the use of standard machine learning algorithms time and computation intensive. Random projection is emerging as a flexible and effective means for dimensionality reduction, particularly when the input signal has a sparse representation in some family of basis functions. This work examines the effect of the dimensionality of the sparse representation and random projection basis on 2-class separability using several popular machine learning algorithms. In particular, the Discrete Cosine Transform (DCT) basis was chosen as the input space, and SVM, Naive Bayes and Multilayer-Perceptron were used for machine learning. Results showed that the correct classification rate using random projection was comparable to classification in the input domain when the dimensionality of random projection vectors was on the order of the dimensionality of the input domain.
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