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Random projection in dimensionality reduction: Applications to image and text data (2001)
| Content Provider | CiteSeerX |
|---|---|
| Author | Bingham, Ella Mannila, Heikki |
| Description | In Knowledge Discovery and Data Mining 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 signicantly 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. |
| File Format | |
| Language | English |
| Publisher | ACM Press |
| Publisher Date | 2001-01-01 |
| Access Restriction | Open |
| Subject Keyword | Noiseless Image Conventional Dimensionality Reduction Method Random Projection Powerful Method Theoretical Result Present Experimental Result Additional Computational Saving Text Data Dimensionality Reduction High Dimensionality Data Vector Burdensome Computation Text Document Information Retrieval Sparse Random Matrix Application Area Principal Component Analysis Random Lower-dimensional Subspace Yield Result Empirical Result Dimensionality Reduction Tool |
| Content Type | Text |
| Resource Type | Article |
