document: Random projection in dimensionality reduction : applications to image and text data Random projections have recently emerged as a powerful method for dimensionality reduction . Theoretical results indicate that the method preserves distances quite nicely ; however , empirical results are sparse . We present experimental results on using random projection as a dimensionality reduction tool in a number of cases , where the high dimensionality of the data would otherwise lead to burden-some computations . Our application areas are the processing of both noisy and noiseless images , and information retrieval in text documents . We show that projecting the data onto a random lower-dimensional subspace yields results comparable to conventional dimensionality reduction methods such as principal component analysis : the similarity of data vectors is preserved well under random projection . However , using random projections is computationally significantly less expensive than using , e.g. , principal component analysis . We also show experimentally that using a sparse random matrix gives additional computational savings in random projection .
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