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KDD
2001
ACM

Random projection in dimensionality reduction: applications to image and text data

11 years 1 months ago
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 burdensome 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 a...
Ella Bingham, Heikki Mannila
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2001
Where KDD
Authors Ella Bingham, Heikki Mannila
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