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NIPS
2001

Constructing Distributed Representations Using Additive Clustering

8 years 10 months ago
Constructing Distributed Representations Using Additive Clustering
If the promise of computational modeling is to be fully realized in higherlevel cognitive domains such as language processing, principled methods must be developed to construct the semantic representations used in such models. In this paper, we propose the use of an established formalism from mathematical psychology, additive clustering, as a means of automatically constructing binary representations for objects using only pairwise similarity data. However, existing methods for the unsupervised learning of additive clustering models do not scale well to large problems. We present a new algorithm for additive clustering, based on a novel heuristic technique for combinatorial optimization. The algorithm is simpler than previous formulations and makes fewer independence assumptions. Extensive empirical tests on both human and synthetic data suggest that it is more effective than previous methods and that it also scales better to larger problems. By making additive clustering practical, w...
Wheeler Ruml
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where NIPS
Authors Wheeler Ruml
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