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• W1492061079 abstract "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. Once vertices are assigned locations in a Euclidean space, the locations can be used to browse music and to generate playlists.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 Nystrom 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." @default.
• W1492061079 created "2016-06-24" @default.
• W1492061079 creator A5073032636 @default.
• W1492061079 date "2003-12-09" @default.
• W1492061079 modified "2023-09-24" @default.
• W1492061079 title "Fast embedding of sparse music similarity graphs" @default.
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