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Yariv Aizenbud: Matrix Decomposition using Randomized Algorithms 28.6.

Viimeisin muutos keskiviikko 07. kesäkuuta 2017, 12.38
Milloin 28.06.2017
alkaa 10.00 loppuu 12.00
Missä AgC 231
Yhteyshenkilön nimi
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Lecturer: M.Sc. Yariv Aizenbud

Abstract:
Matrix decompositions, and especially SVD, are very important tools in data analysis. When big data is processed, the  computation of matrix decompositions becomes expensive and impractical. In recent years, several algorithms, which approximate matrix decomposition, have been developed. These algorithms are based on metric conservation features for linear spaces of random projections. 

We present a randomized method based on sparse matrix distribution that achieves a fast approximation with bounded error for low rank matrix decomposition. We will also see the practical results of the algorithm when decomposing real big-data matrices.