Neighborhood components analysis and machine learning
| Název práce v češtině: | Analýza sousedních komponent a strojové učení |
|---|---|
| Název v anglickém jazyce: | Neighborhood components analysis and machine learning |
| Klíčová slova: | KNN, NCA, FNCA, kernel trick, TSKNN, TSNCA, klasifikace |
| Klíčová slova anglicky: | KNN, NCA, FNCA, kernel trick, TSKNN, TSNCA, classification |
| Akademický rok vypsání: | 2017/2018 |
| Typ práce: | bakalářská práce |
| Jazyk práce: | angličtina |
| Ústav: | Katedra pravděpodobnosti a matematické statistiky (32-KPMS) |
| Vedoucí / školitel: | prof. RNDr. Jaromír Antoch, CSc. |
| Řešitel: | skrytý - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 31.10.2017 |
| Datum zadání: | 31.10.2017 |
| Datum potvrzení stud. oddělením: | 15.12.2017 |
| Datum a čas obhajoby: | 13.09.2018 09:00 |
| Datum odevzdání elektronické podoby: | 17.05.2018 |
| Datum odevzdání tištěné podoby: | 20.07.2018 |
| Datum proběhlé obhajoby: | 13.09.2018 |
| Oponenti: | doc. RNDr. Matúš Maciak, Ph.D. |
| Zásady pro vypracování |
| Neighbourhood components analysis (NCA) aims at "learning" a distance metric by finding a linear transformation of input data such that the average leave-one-out classification performance is maximized in the transformed space. The key insight to the algorithm is that a matrix A corresponding to the transformation can be found by defining a differentiable objective function for A, followed by use of an iterative solver such as conjugate gradient descent. One of the benefits of this algorithm is that the number of classes can be determined as a function of A, up to a scalar constant. This use of the algorithm therefore addresses the issue of model selection.
Main goals of the thesis are as follows: - to describe the basic algorithms; - to give characterization of their properties; - to compare considered algorithms with another approaches traditionally used for classification and model selection as, e.g. SVM; - based on the real nontrivial examples to illustrate advantages and disadvantages of selected approach. |
| Seznam odborné literatury |
| 1) Jacob Goldberger, Sam Roweis, Geoff Hinton, Ruslan Salakhutdinov. Neighborhood Components Analysis.
Department of Computer Science, University of Toronto Working paper 2) Wei Yanga, Kuanquan Wang, Wangmeng Zuo. Fast neighborhood component analysis. Neurocomputing 83, 2012, 31-37. 3) Chen Qina, Shiji Song, Gao Huang, Lei Zhu. Unsupervised neighborhood component analysis for clustering. Neurocomputing 168, 2015, 609-617. 4) Bernhard Scholkopf, Alexander Smola, Klaus-Robert Muller. Nonlinear component analysis as a kernel eigenvalue problem. Max-Planck-Institut fur biologische Kybernetik Working paper. 5) https://github.com/danoneata/fast-nca 6) https://wiki.math.uwaterloo.ca/statwiki/index.php?title=neighbourhood_Components_Analysis 7) Matlab 2016b 9) Everitt, B. S., Landau, S., Leese, M. and Stahl, D. Miscellaneous Clustering Methods, in Cluster Analysis, 5th Edition, John Wiley & Sons, Ltd, Chichester, UK, 2011. 10) Samworth, R.J. Optimal weighted nearest neighbor classifiers. Annals of Statistics. 40 (5): 2733-2763, 2012. 11) Bernhard Scholkopf, The kernel trick for distances, Microsoft Research technical report, Cambridge, UK. |