|
Yu.S. Popkov Effectiveness estimation of matrices compression in the procedures of randomized machine learning |
|
AbstractThe method of estimation effectiveness of the matrices compressions. That oriented to the procedures randomized machine learning. It is proposed to measure of effectiveness in the term of the Kullback-Leibler function. Keywords: randomized machine learning, entropy, KL-distance pp. 3-7 References1. Yu. S. Popkov, Yu. A. Dubnov, A. Yu. Popkov. Randomized Machine Learning: Statement, Solution, Applications //Proceedings of 2016 IEEE 8-th International Conference on Intelligent Systems (IS16). September 4-6, 2016. Sofia, Bulgaria, P.27-39. 2. Yuri S. Popkov, Zeev Volkovich, Yuri A. Dubnov, Renata Avros and Elena Ravve. // Entropy '2'-Soft Classification of Objects // Entropy, 2017, Vol. 19, Iss. 4, No.178. 3. Popkov Y.S., Dubnov Y.A. Entropy-robust randomized forecasting under small sets of retrospective data. Automation and Remote Control, 2016,v.77, No.5, p.839-854. 4. Bruckstein A.M., Donoho D.L., Elad M. From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images. SIAM Rev. 2009, v.51, No.1, p.34-81. 5. Kendall M, Stewart A. Statisticheskie vivodi I sviazi, Nauka , 1973. 6. Jollife I.T. Principal Component Analysis. N.Y. Springer-Verlag, 2002. 7. Polyak B.T., Hlebnikov M.V. Metod glavnih komponent: robastnie versii // Avtomatika I telemehanika, 2017, №3, с.130-148. 8. Popkov Y.S. Entropiinii metod szhatia matric so sluchainimi znacheniami elemntov // ITVS, 2018, №1, с. 9. Kullback S., Leibler R.A. On information and Sufficiency. Ann. of Math. Statistics, 1951, v.22(1), p. 79-86. 10. Zhang Y., Li S., Wang T., Zhang Z. Divergence-based feature selection for separate classes. Neurocomputing, 2013, v.101, p. 32-42.
|