Abstract
For discovering hidden (latent) variables in real-world, non-gaussian data streams or an n-dimensional cloud of data points, SVD suffers from its orthogonality constraint. Our proposed method, “AutoSplit”, finds features which are mutually independent and is able to discover non-orthogonal features. Thus, (a) finds more meaningful hidden variables and features, (b) it can easily lead to clustering and segmentation, (c) it surprisingly scales linearly with the database size and (d) it can also operate in on-line, single-pass mode. We also propose “Clustering-AutoSplit”, which extends the feature discovery to multiple feature/bases sets, and leads to clean clustering. Experiments on multiple, real-world data sets show that our method meets all the properties above, outperforming the state-of-the-art SVD.
Supported in part by Japan-U.S. Cooperative Science Program of JSPS; grants from JSPS and MEXT (#15017207, #15300027); the NSF No. IRI-9817496, IIS-9988876, IIS-0113089, IIS-0209107, IIS-0205224, INT-0318547, SENSOR-0329549, EF-0331657; the Pennsylvania Infrastructure Technology Alliance No. 22-901-0001; DARPA No. N66001-00-1-8936; and donations from Intel and Northrop-Grumman.
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Pan, JY., Kitagawa, H., Faloutsos, C., Hamamoto, M. (2004). AutoSplit: Fast and Scalable Discovery of Hidden Variables in Stream and Multimedia Databases. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_62
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DOI: https://doi.org/10.1007/978-3-540-24775-3_62
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