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CACM
2010

Faster dimension reduction

9 years 3 months ago
Faster dimension reduction
Data represented geometrically in high-dimensional vector spaces can be found in many applications. Images and videos, are often represented by assigning a dimension for every pixel (and time). Text documents may be represented in a vector space where each word in the dictionary incurs a dimension. The need to manipulate such data in huge corpora such as the web and to support various query types gives rise to the question of how to represent the data in a lower-dimensional space to allow more space and time efficient computation. Linear mappings are an attractive approach to this problem because the mapped input can be readily fed into popular algorithms that operate on linear spaces (such as principal-component analysis, PCA) while avoiding the curse of dimensionality. The fact that such mappings even exist became known in computer science following seminal work by Johnson and Lindenstrauss in the early 1980s. The underlying technique is often called "random projection." T...
Nir Ailon, Bernard Chazelle
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where CACM
Authors Nir Ailon, Bernard Chazelle
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