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Effect of HFS Based Feature Selection on Cluster Analysis
Malásek, Jan ; Klusáček, Jan (referee) ; Honzík, Petr (advisor)
Master´s thesis is focused on cluster analysis. Clustering has its roots in many areas, including data mining, statistics, biology and machine learning. The aim of this thesis is to elaborate a recherche of cluster analysis methods, methods for determining number of clusters and a short survey of feature selection methods for unsupervised learning. The very important part of this thesis is software realization for comparing different cluster analysis methods focused on finding optimal number of clusters and sorting data points into correct classes. The program also consists of feature selection HFS method implementation. Experimental methods validation was processed in Matlab environment. The end of master´s thesis compares success of clustering methods using data with known output classes and assesses contribution of feature selection HFS method for unsupervised learning for quality of cluster analysis.
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Unsupervised Anomaly Detection in Image
Salvet, Lukáš ; Herout, Adam (referee) ; Juránek, Roman (advisor)
This thesis deals with anomaly detection on industrial products. The main requirement was that the method required as little data with anomalies as possible at the time of construction and that it was easily applicable to different types of products. Neural network that is indirectly taught to find differences between two pictures is designed and described in this thesis. The anomaly detection itself should take place based on the representation of input data in latent space or in combination with a reconstruction loss. Four different method modifications have been designed and tested. The testing was mainly carried out on the MVTec AD dataset, which contains industrial products. Unfortunately the assumption that if the network is taught to look for differences the latent space will be interpreted better was not confirmed. Therefore the method was evaluated in a reconstructive error mode in~which it achieves comparable results with other methods. The result is insufficient for use in practice.
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Metody pro shlukování a vyhledávání v obrazových datech z elektronových mikroskopů
Plachý, Tomáš ; Hříbek, David (referee) ; Čadík, Martin (advisor)
This thesis deals with the problem of clustering images from electron microscopy. These images can be clustered by visual similarity or by metadata, which describe the settings of the microscope. The goal of this thesis is to compare these two clustering approaches and to explore the possibility of utilizing clustering to split a set of pictures into two parts - one containing correct pictures and the other containing pictures which capture an error during automatized work of an electron microscope. Conclusion of this thesis is that visual differences and differences in metadata between correct and errorous images from electron microscopy are so small, that they cannot be distinguished by unsupervised clustering techniques. However, a positive contribution of this work is demonstration of usability of the methods chosen in this thesis for clustering images into groups corresponding with different phases of work of the microscope, which will make the manual analysis of these pictures easier.
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Unsupervised Evaluation of Speaker Recognition System
Odehnal, Ondřej ; Plchot, Oldřich (referee) ; Matějka, Pavel (advisor)
Tato práce je vystavěna nad moderním systémem pro rozpoznávání mluvčího (SID) založeného na x-vektorech. Cílem bakalářské práce je navrhnout a experimentálně vyhodnotit techniky pro evaluaci SID systému za použití audio nahrávek bez anotace tj. bez znalosti mluvčího. Pro tento účel je z každé nahrávky bez anotace vytvořen embedding. Ty se poté používají pro shlukování nahrávek a následné vytvoření pseudo-anotací. Na těchto anotacích se SID systém evaluuje pomocí equal error rate (EER) metriky. Za účelem vytvoření pseudo-anotací byly navrženy tyto shlukovací algoritmy učení bez učitele: K-means, Gaussian mixture models (GMM) a aglomerativní shlukování. Po testování vyšel jakožto nejlepší experimentální postup K-means se Silhouette metrikou, která používá kosinovou podobnost jako míru vzdálenosti. Nejlepší metoda dosáhla 5,72 % EER s referenčním EER = 5,15 %, které bylo spočítané se znalostí anotace na části datasetu SITW dev-core-core. Podobné výsledky byly získány na části datasetu SITW eval-core-core s odhadnutým EER = 5,86 % a referenčním 5,08 %. Rozdíl mezi hodnotami tvoří 0,57 % pro eval-core-core a 0, 78% pro dev-core-core. Další testy na NIST SRE16 a VoxCeleb1 datasetech byly provedeny za účelem ověření správnosti navrženého postupu. Obecně se dá říct, že navržený testovací postup měl chybu přibližně 1 %, což je poměrně dobrý výsledek pro algoritmus učení bez učitele.
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Automatic image classification
Ševčík, Zdeněk ; Miklánek, Štěpán (referee) ; Sikora, Pavel (advisor)
The aim of this thesis is to explore clustering algorithms of machine unsupervised learning, which can be used for image database classification by similarity. For chosen clustering algorithms is written up a theoretical basis. For better classification of used database this thesis deals with different methods of image preprocessing. With these methods the features from image are extracted. Next the thesis solves of implementation of preprocessing methods and practical application of clustering algorithms. In practical part is programmed aplication in Python programming language, which classifies the database of images into classes by similarity. The thesis tests all of used methods and at the end of the thesis is processed searches of results.
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Data Mining Case Study in Python
Stoika, Anastasiia ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This thesis focuses on basic concepts and techniques of the process known as knowledge discovery from data. The goal is to demonstrate available resources in Python, which enable to perform the steps of this process. The thesis addresses several methods and techniques focused on detection of unusual observations, based on clustering and classification. It discusses data mining task for data with the limited amount of inspection resources. This inspection activity should be used to detect unusual transactions of sales of some company that may indicate fraud attempts by some of its salespeople.
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Demonstrational Program for IZU Course
Hreha, Tomáš ; Šůstek, Martin (referee) ; Zbořil, František (advisor)
This bachelor thesis deals with the design of application for visualization of fundamental algorithms of artificial intelligence. The first part describes theoretical part of implemented topics and methods, next part briefly describes used technologies, reasons why they were used and their practical usage in context of result application. The next part is dedicated to user interface, its main components and describes ways how application interacts with user and how user can interact with application. The last part contains comparison with original demo applications and summarize results of application testing.
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Classification of Testing Maneuvers from Flight Data
Funiak, Martin ; Dittrich, Petr (referee) ; Chudý, Peter (advisor)
Zapisovač letových údajů je zařízení určené pro zaznamenávání letových dat z různých senzorů v letadlech. Analýza letových údajů hraje důležitou roli ve vývoji a testování avioniky. Testování a hodnocení charakteristik letadla se často provádí pomocí testovacích manévrů. Naměřená data z jednoho letu jsou uložena v jednom letovém záznamu, který může obsahovat několik testovacích manévrů. Cílem této práce je identi kovat základní testovací manévry s pomocí naměřených letových dat. Teoretická část popisuje letové manévry a formát měřených letových dat. Analytická část popisuje výzkum v oblasti klasi kace založené na statistice a teorii pravděpodobnosti potřebnou pro pochopení složitých Gaussovských směšovacích modelů. Práce uvádí implementaci, kde jsou Gaussovy směšovací modely použité pro klasifi kaci testovacích manévrů. Navržené řešení bylo testováno pro data získána z letového simulátoru a ze skutečného letadla. Ukázalo se, že Gaussovy směšovací modely poskytují vhodné řešení pro tento úkol. Další možný vývoj práce je popsán v závěrečné kapitole.
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Searching Acoustic Patterns in Speech Data without Recognition
Skácel, Miroslav ; Fapšo, Michal (referee) ; Černocký, Jan (advisor)
This work investigates into methods for words, word phrases and longer segments detection in large speech data sets in an unsupervised way. At first, basics for the given topic and principles of modern methods for searching of repeating objects are introduced. The representation and segmentation of the input data are described. Techniques for object detection in speech are presented. The description of found motifs modelling follows. The next step defi nes data sets for experiments in which spoken term detection by an example is performed. The system requirements are described. In the conclusion, the work is summarised and suggestions for further development are discussed.
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