This paper applies fast sparse multidimensional scaling (MDS) to a large
graph of music similarity, with 267K vertices that represent artists, al-
bums, 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 Nyström ap-
proximation 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.
%0 Journal Article
%1 �δ
%A Platt, John C.
%D 2004
%K thema:playlistpredictionviametricembedding
%P 8
%T Fast Embedding of Sparse Music Similarity Graphs
%U http://research.microsoft.com/pubs/68916/fastsparsemusic.pdf
%X This paper applies fast sparse multidimensional scaling (MDS) to a large
graph of music similarity, with 267K vertices that represent artists, al-
bums, 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 Nyström ap-
proximation 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.
@article{�δ,
abstract = {This paper applies fast sparse multidimensional scaling (MDS) to a large
graph of music similarity, with 267K vertices that represent artists, al-
bums, 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 Nyström ap-
proximation 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.
},
added-at = {2013-11-08T17:34:23.000+0100},
author = {Platt, John C.},
biburl = {https://www.bibsonomy.org/bibtex/2916f953fe2407c0efd71a2c3e71b12e3/florian.pf},
description = {nips.dvi - fastsparsemusic.pdf},
interhash = {d4b337e6db724818a40267c5de420d53},
intrahash = {916f953fe2407c0efd71a2c3e71b12e3},
keywords = {thema:playlistpredictionviametricembedding},
pages = 8,
timestamp = {2013-11-08T17:34:23.000+0100},
title = {Fast Embedding of Sparse Music Similarity Graphs
},
url = {http://research.microsoft.com/pubs/68916/fastsparsemusic.pdf},
year = 2004
}