| Publisher | Microsoft | ||
|---|---|---|---|
| Format | 146.8KB PDF | Date added | 09 Jun 2007 |
| Topics | Embedded Systems, Software Engineering | ||
| Downloads | 18 | ||
This paper applies fast sparse MultiDimensional Scaling (MDS) to a large graph of music similarity, with 267K vertices that represent artists, albums, and tracks; and 3.22M edges that represent similarity between those entities. MDS on very large sparse graphs can be effectively performed by a family of algorithms called Rectangular Dijsktra (RD) MDS algorithms. These RD algorithms operate on a dense rectangular slice of the distance matrix, created by calling Dijsktra a constant number of times. Two RD algorithms are compared: Landmark MDS, which uses the Nystrm approximation to perform MDS; and a new algorithm called Fast Sparse Embedding, which uses FastMap. These algorithms compare favorably to Laplacian Eigenmaps, both in terms of speed and embedding quality.
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