Spectral analysis of data

Yossi Azar*, Amos Fiat, Anna Karlin, Frank McSherry, Jared Saia

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


Experimental evidence suggests that spectral techniques are valuable for a wide range of applications. A partial list of such applications include (i) semantic analysis of documents used to cluster documents into areas of interest, (ii) collaborative filtering - the reconstruction of missing data items, and (iii) determining the relative importance of documents based on citation/link structure. Intuitive arguments can explain some of the phenomena that has been observed but little theoretical study has been done. In this paper we present a model for framing data mining tasks and a unified approach to solving the resulting data mining problems using spectral analysis. These results give strong justification to the use of spectral techniques for latent semantic indexing, collaborative filtering, and web site ranking.

Original languageEnglish
Pages (from-to)619-626
Number of pages8
JournalConference Proceedings of the Annual ACM Symposium on Theory of Computing
StatePublished - 2001
Event33rd Annual ACM Symposium on Theory of Computing - Creta, Greece
Duration: 6 Jul 20018 Jul 2001


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