The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Spectral analysis of data
Abstract
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.
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Social information filtering: algorithms for automating “word of mouth”
Shardanand, U., & Maes, P. (1995) · Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '95 · doi:10.1145/223904.223931 · Cited by 2,823
Authoritative sources in a hyperlinked environment
Kleinberg, J. M. (1998) · Symposium on Discrete Algorithms · doi:10.5555/314613.315045 · Cited by 1,814
Using Linear Algebra for Intelligent Information Retrieval
Berry, M. W., Dumais, S. T., & O’Brien, G. W. (1995) · SIAM Review · doi:10.1137/1037127 · Cited by 1,481
Recommending and evaluating choices in a virtual community of use
Hill, W., Stead, L., Rosenstein, M., & Furnas, G. (1995) · Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '95 · doi:10.1145/223904.223929 · Cited by 1,129
Recommendation as classification: using social and content-based information in recommendation
Basu, C., Hirsh, H., & Cohen, W. W. (1998) · Cited by 955
Latent semantic indexing: a probabilistic analysis
Papadimitriou, C. H., Tamaki, H., Raghavan, P., & Vempala, S. (1998) · Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems · doi:10.1145/275487.275505 · Cited by 863
Matrices, Vector Spaces, and Information Retrieval
Berry, M. W., Drmac, Z., & Jessup, E. R. (1999) · SIAM Review · doi:10.1137/s0036144598347035 · Cited by 692
The eigenvalues of random symmetric matrices
Füredi, Z., & Komlós, J. (1981) · Combinatorica · doi:10.1007/bf02579329 · Cited by 633
Cited by
Exact Matrix Completion via Convex Optimization
Candès, E. J., & Recht, B. (2009) · Foundations of Computational Mathematics · doi:10.1007/s10208-009-9045-5 · Cited by 4,877
One-Class Collaborative Filtering
Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., & Yang, Q. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.16 · Cited by 1,015
Fast maximum margin matrix factorization for collaborative prediction
Rennie, J. D. M., & Srebro, N. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102441 · Cited by 939
On clusterings: Good, bad and spectral
Kannan, R., Vempala, S., & Vetta, A. (2004) · Journal of the ACM · doi:10.1145/990308.990313 · Cited by 834
The Optimal Hard Threshold for Singular Values is \(4/\sqrt {3}\)
Gavish, M., & Donoho, D. L. (2014) · IEEE Transactions on Information Theory · doi:10.1109/tit.2014.2323359 · Cited by 675
Metadata
- Authors
- Azar, Y., Fiat, A., Karlin, A., McSherry, F., & Saia, J.
- Venue
- Proceedings of the thirty-third annual ACM symposium on Theory of computing
- Publisher
- ACM
- Published
- 2001-07-06
- Pages
- 619-626
- OpenAlex
- W2021680564
- Cited by
- 289
- References
- 13
- FWCI
- 13.40
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Azar, Y., Fiat, A., Karlin, A., McSherry, F., & Saia, J. (2001). Spectral analysis of data. Proceedings of the thirty-third annual ACM symposium on Theory of computing. https://doi.org/10.1145/380752.380859
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
