Sharif Digital Repository

Increasing information in real world needs the special instrument for data saving, cleaning and processing. Data cleaning is so important steps in machine learning application that include various kind of procedures such as, duplicate detection, fill out missing value and outlier detection. Outliers are observation, which deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism. Many researches has been carried out in the machine learning field with regards to the outlier detection that has applications in real world, like: Intrusion detection for network security, fraud detection in credit cards, fault detection for security in critical... 

About a century, “Eye Tracking” has been studied, and it has two definitions: • The process of measuring the point of gaze (where one is looking). • The process of measuring the motion of an eye relative to the head. Eye tracking technology has been used in many fields such as psychology. However, applications of this technology has been recently considered in marketing, computer interfacing, entertainment, training and so forth. Since pupil is a distinc area in eye images, pupil detection is one of the effective solutions of eye tracking. In most of the pupil detection approaches, the edge points of the pupil contour are detected firstly, and then the optimal ellipse is fitted to them.... 

Image registration is an important preprocessing step in analysis of medical images. Detection, Treatment plan, disease grows process analysis and assistance in surgical applications are some of medical images applications. We need to be able to compare different modalities in medical images such as X-ray, PET, MRI, and CT... , or sometimes doctors need to take images of a patient in a same modality but in different times and directions. In which in order to be able to do theses comparisons we need to first align these images by using image registration methods. Image registration is an image processing method in which tries to find a geometrical transformation that would map different... 

Although clustering algorithms such as k-means and probabilistic clustering are quite popular and widely used nowadays, their performance are too sensitive to the presence of outliers where Even few outliers can compromise the ability of these algorithms to extract hidden data substructures. In this thesis, after going through the basics of some optimization methods such as BCD, EM, and MM, in Section 2 and a review of relevant clustering methods in Section 3, we explore the results of [Forero, et al., Robust clustering using outlier-sparsity regularization. IEEE Trans. Signal Process. (60), 2012] in Sections 4 and 5 where the outliers are handled by introducing a regularization term in the... 

With the emergence of massive datasets, storing all of the data in memory is not feasible for many problems. This fact motivated the introduction of new data processing models such as the streaming model. In this model, data points arrive one by one and the available memory is too small to store all of the data points. For many problems, more recent data points are more important than the old ones. The sliding window model captures this fact by trying to find the solution for the n most recent data points using only o(n) memory. The k-center problem is an important optimization problem in which given a graph, we are interested in labeling k vertices of the graph as centers such that the... 

In today’s world, knowledge of the statistical behavior of noise can tremendously affect the accuracy of target detection in radar systems. Therefore, radar systems commonly collect a secondary dataset of homogeneous noise and estimate the statistics of the gathered data, prior to attempting target detection. Specifically, in the case of Gaussian noise with a mean of zero, the entire statistical information of the noise is encoded in its covariance matrix. In practice, however, the challenge is that the training samples do not purely contain homogeneous noise. In fact, some samples contain non-homogeneous outlier signals that do not have the same distribution as the noise samples. In this... 

Clustering is a fundamental problem for data analysis, and it has a lot of variants. In this thesis we focused on the k-center problem, which is one of the most popular and well-studied variants of clustering. In this problem we are given a metric set of points called X, and a parameter k ⩽ |X|. Our goal is to find a set of k centers in X, minimizing the maximum distance of any point of X from its closest center. This thesis has worked on a version of the problem that is harder to solve. we have an extra parameter called z, which represents the maximum number of points that there is no need to be clustered, and we refer to them as outliers. The growth of data that needs to be processed makes...