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Approximating Matrix Multiplicationfor Pattern Recognition
| Content Provider | Semantic Scholar |
|---|---|
| Author | Cohen, Tamir Lewis, David D. |
| Copyright Year | 1997 |
| Abstract | Many pattern recognition tasks, including estimation, classiication, and the nding of similar objects, make use of linear models. The fundamental operation in such tasks is the computation of the dot product between a query vector and a large database of instance vectors. Often we are interested primarily in those instance vectors which have high dot products with the query. We present a random sampling based algorithm that enables us to identify, for any given query vector, those instance vectors which have large dot products, while avoiding explicit computation of all dot products. We provide experimental results that demonstrate considerable speedups for text retrieval tasks. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.research.att.com/~lewis/papers/cohen97.ps |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
