03327ntm a22005057i 4500 000701097 CZ-PrVSE 20230827080715.0 m d cr n|||||||||| 230827s2023 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Iqbal, Sobia ISIS:158094 dis t-SNE : a Machine-Learning Method for Data Dimensionality Reduction and Visualization eng t-SNE : a Machine-Learning Method for Data Dimensionality Reduction and Visualization Sobia Iqbal 2023 ?? stran : digital, PDF soubor Vedoucí práce: Miroslav Plašil Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2023 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2023 In the thesis, t-SNE is introduced, discussed , and applied on various synthetic and non synthetic datasets to outline the inner working of t-SNE. The objective of the thesis is to investigate all t-SNE parameters in general, perplexity in particular. This study is intended to find a way of choosing optimal perplexity value. We use two different approaches to find optimal perplexity value. First, we plot t-SNE with set of perplexity values and choose the best plot. Than we plot Kullback-Leibler divergence against perplexity for each batch of data and choose the perplexity suggested by Kullback-Leibler divergence as the author of the t-SNE Laurens van der Maaten said in his original paper "The kullback-Liebler divergence between the joint probability distribution of high-dimensional similarities and low-dimension similarities is minimised by using gradient descent ". Therefor choosing the best perplexity by minimizing the Kullback-Leibler divergence appeared fair. In last we compared the performance from both methods to check which method provides the best results. The research questions investigated throughout the thesis are as follows: Difference between Implementation of Laurens Van der Maaten t-SNE code and t-SNE Package. Second, Comparison of manually and KL chosen perplexity and to conclude which one is more reliable. Způsob přístupu: Internet data analysis and modeling [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas SNE t-SNE Kullback–Leibler divergence Perplexity Plašil, Miroslav ola2012709459 ths Fojtík, Jan ISIS:87865 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/78655/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/78655 Hlavní práce https://insis.vse.cz/zp/78655/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/78655/posudek/oponent/79933 Oponentura https://insis.vse.cz/zp/78655/podrobnosti dc:identifier NEPOSILAT VSKP vse78655 230824 78655