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    Fault Detection and Smart Monitoring of Industrial Fans Based on Vibration Signals

    , M.Sc. Thesis Sharif University of Technology Moeeni, Hamed (Author) ; Manzuri Shalmani, Mohammad Taghi (Supervisor)
    Abstract
    Data Oriented Smart Monitoring for Industrial Machineries include approaches for fault detection and prognosis which only rely on non-stationary signals sampled from sensors and do not rely on physical model of machineries nor expert knowledge. Fault detection is task of determining state of machinery in present moment using past data. But in Prognosis focus is on predicting future state of machinery using past data. Most researches in this category are based on supervised algorithms, but in many applications labeling data is expensive. In this thesis some approaches for semi-superviseddiagnosis, based on markov random walk an K-NN have been implemented, also some improvements for K-NN have... 

    Semi-supervised Learning and its Application to Image Categorization

    , M.Sc. Thesis Sharif University of Technology Farajtabar, Mehrdad (Author) ; Rabiee, Hamid Reza (Supervisor)
    Abstract
    Traditional methods for data classification only make use of the labeled data. However, in most of the applications, labeling the unlabeled data is expensive, time consuming and requires expert knowledge. To overcome these problems, Semi-supervised Learning (SSL) methods have become an area of recent research that aim to effectively addressing the problem of limited labeled data.One of the recently introduced SSL methods is the classification based on geometric structure of the data, namely the data manifold. In this approach unlabeled data is utilized to recover the underlying structure of the data. The common assumption is that despite of being represented in a high dimensional space, data... 

    Automatic image annotation using semi-supervised generative modeling

    , Article Pattern Recognition ; Volume 48, Issue 1 , January , 2015 , Pages 174-188 ; 00313203 (ISSN) Amiri, S. H ; Jamzad, M ; Sharif University of Technology
    Elsevier Ltd  2015
    Abstract
    Image annotation approaches need an annotated dataset to learn a model for the relation between images and words. Unfortunately, preparing a labeled dataset is highly time consuming and expensive. In this work, we describe the development of an annotation system in semi-supervised learning framework which by incorporating unlabeled images into training phase reduces the system demand to labeled images. Our approach constructs a generative model for each semantic class in two main steps. First, based on Gamma distribution, a generative model is constructed for each semantic class using labeled images in that class. The second step incorporates the unlabeled images by using a modified EM... 

    An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams

    , Article Knowledge and Information Systems ; Volume 46, Issue 3 , 2016 , Pages 567-597 ; 02191377 (ISSN) Hosseini, M. J ; Gholipour, A ; Beigy, H ; Sharif University of Technology
    Springer-Verlag London Ltd 
    Abstract
    Recent advances in storage and processing have provided the possibility of automatic gathering of information, which in turn leads to fast and continuous flows of data. The data which are produced and stored in this way are called data streams. Data streams are produced in large size, and much dynamism and have some unique properties which make them applicable to model many real data mining applications. The main challenge of streaming data is the occurrence of concept drift. In addition, regarding the costs of labeling of instances, it is often assumed that only a small fraction of instances are labeled. In this paper, we propose an ensemble algorithm to classify instances of non-stationary... 

    Transductive multi-label learning from missing data using smoothed rank function

    , Article Pattern Analysis and Applications ; Volume 23, Issue 3 , 2020 , Pages 1225-1233 Esmaeili, A ; Behdin, K ; Fakharian, M. A ; Marvasti, F ; Sharif University of Technology
    Springer  2020
    Abstract
    In this paper, we propose two new algorithms for transductive multi-label learning from missing data. In transductive matrix completion (MC), the challenge is prediction while the data matrix is partially observed. The joint MC and prediction tasks are addressed simultaneously to enhance accuracy in comparison with separate tackling of each. In this setting, the labels to be predicted are modeled as missing entries inside a stacked matrix along the feature-instance data. Assuming the data matrix is of low rank, we propose a new recommendation method for transductive MC by posing the problem as a minimization of the smoothed rank function with non-affine constraints, rather than its convex... 

    An Efficient semi-supervised multi-label classifier capable of handling missing labels

    , Article IEEE Transactions on Knowledge and Data Engineering ; Volume 31, Issue 2 , 2019 , Pages 229-242 ; 10414347 (ISSN) Hosseini Akbarnejad, A ; Soleymani Baghshah, M ; Sharif University of Technology
    IEEE Computer Society  2019
    Abstract
    Multi-label classification has received considerable interest in recent years. Multi-label classifiers usually need to address many issues including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods represent the label assignments in a low-dimensional space. Many state-of-the-art embedding-based methods use a linear dimensionality reduction to map the label assignments to a low-dimensional space. However, by doing so, these... 

    Network-based direction of movement prediction in financial markets

    , Article Engineering Applications of Artificial Intelligence ; Volume 88 , February , 2020 Kia, A. N ; Haratizadeh, S ; Shouraki, S. B ; Sharif University of Technology
    Elsevier Ltd  2020
    Abstract
    Market prediction has been an important research problem for decades. Having better predictive models that are both more accurate and faster has been attractive for both researchers and traders. Among many approaches, semi-supervised graph-based prediction has been used as a solution in recent researches. Based on this approach, we present two prediction models. In the first model, a new network structure is introduced that can capture more information about markets’ direction of movements compared to the previous state of the art methods. Based on this novel network, a new algorithm for semi-supervised label propagation is designed that is able to prediction the direction of movement faster... 

    Video Classification Usinig Semi-supervised Learning Methods

    , M.Sc. Thesis Sharif University of Technology Karimian, Mahmood (Author) ; Kasaei, Shohreh (Supervisor)
    Abstract
    In large databases, availability of labeled training data is mostly prohibitive in classification. Semi-supervised algorithms are employed to tackle the lack of labeled training data problem. Video databases are the epitome for such a scenario; that is why semi-supervised learning has found its niche in it. Graph-based methods are a promising platform for semi-supervised video classification. Based on the multiview characteristic of video data, different features have been proposed (such as SIFT, STIP and MFCC) which can be utilized to build a graph. In this project, we have proposed a new classification method which fuses the results of manifold regularization over different graphs. Our... 

    Application of Semi-Supervised Learning in Image Processing

    , M.Sc. Thesis Sharif University of Technology Mianjy, Poorya (Author) ; Rabiee, Hamidreza (Supervisor)
    Abstract
    In recent years, the emergence of semi-supervised learning methods has broadened the scope of machine learning, especially for pattern classification. Besides obviating the need for experts to label the data, efficient use of unlabeled data causes a significant improvement in supervised learning methods in many applications. With the advent of statistical learning theory in the late 80's, and the emergence of the concept of regularization, kernel learning has always been in deep concentration. In recent years, semi-supervised kernel learning, which is a combination of the two above-mentioned viewpoints, has been considered greatly.
    Large number of dimensions of the input data along with... 

    Regularization from the Machine Learning Point of View

    , M.Sc. Thesis Sharif University of Technology Ghaemi, Mohammad Sajjad (Author) ; Daneshgar, Amir (Supervisor)
    Abstract
    In traditional machine learning approaches to classification, one uses only a labeled set to train the classifier. Labeled instances however are often difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotators. Meanwhile unlabeled data may be relatively easy to collect, but there has been few ways to use them. Semi-supervised learning addresses this problem by using large amount of unlabeled data, together with the labeled data, to build better classifiers. Because semi-supervised learning requires less human effort and gives higher accuracy.Formally, this intuition corresponds to estimating a label function f on the graph so that it... 

    A Semi-Supervised Ensemble Learning Algorithm for Nonstationary Data Streams Classification

    , M.Sc. Thesis Sharif University of Technology Hosseini, Mohammad Javad (Author) ; Beigy, Hamid (Supervisor)
    Abstract
    Recent advances in storage and processing, have provided the ability of automatic gathering of information which in turn leads to fast and contineous flow of data. The data which are produced and stored in this way, are named data streams. data streams have many applications such as processing financial transactions, the recorded data of various sensors or the collected data by web sevices. Data streams are produced with high speed, large size and much dynamism and have some unique properties which make them applicable in precise modeling of many real data mining applications. The main challenge of data streams is the occurrence of concept drift which can be in four types: sudden, gradual,... 

    Recognition of Human Activities by Using Machine Learning Methods

    , M.Sc. Thesis Sharif University of Technology Ghazvininejad, Marjan (Author) ; Rabiee, Hamid Reza (Supervisor)
    Abstract
    In this research, we have used machine learning methods to approach the problem of human activity recognition. As the process of labeling the data in this problem is so costly and time consuming, and regarding the copious available unlabeled data, semi supervised methods have a high performance in this problem. In recent years, graph based methods have became very populaer among semi supervised learning methods. However, constructing a graph on the data which presents their structure in a proper manner has remained a main challenge in these methods. One of the causes of this problem is the existance of the shortcut edges. In this report, we will first introduce a method to solve the problem... 

    Online Semi-supervised Learning and its Application in Image Classification

    , M.Sc. Thesis Sharif University of Technology Shaban, Amir Reza (Author) ; Rabiee, Hamid Reza (Supervisor)
    Abstract
    Image classification, i.e. the task of assigning an image to a class chosen from a predefined set of classes, has addressed in this thesis. At first the classifier is divided into two major sub partitions, feature extraction and classifier. Then we show that by using local feature extraction techniques such as BOW the classification accuracy will improve. In addition, using unlabeled data is argued as the fact to deal with high nonlinear structure of features. Recently, many SSL methods have been developed based on the manifold assumption in a batch mode. However, when data arrive sequentially and in large quantities, both computation and storage limitations become a bottleneck. So in large... 

    Improving Graph Construction for Semi-supervised Learning in Computer Vision Applications

    , M.Sc. Thesis Sharif University of Technology Mahdieh, Mostafa (Author) ; Rabiee, Hamid Reza (Supervisor)
    Abstract
    Semi-supervised Learning (SSL) is an extremely useful approach in many applications where unlabeled data can be easily obtained. Graph based methods are among the most studied branches in SSL. Since neighborhood graph is a key component in these methods, we focus on methods of graph construction in this project. Graph construction methods based on Euclidean distance have the common problem of creating shortcut edges. Shortcut edges refer to the edges which connect two nearby points that are far apart on the manifold. Specifically, we show both in theory and practice that using geodesic distance for selecting and weighting edges results in more appropriate neighborhood graphs. We propose an... 

    Adaptation for Evolving Domains

    , M.Sc. Thesis Sharif University of Technology Bitarafan, Adeleh (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)
    Abstract
    Until now many domain adaptation methods have been proposed. A major limitation of almost all of these methods is their assumption that all test data belong to a single stationary target distribution and a large amount of unlabeled data is available for modeling this target distribution. In fact, in many real world applications, such as classifying scene image with gradually changing lighting and spam email identification, data arrives sequentially and the data distribution is continuously evolving. In this thesis, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced and propose the Evolving Domain Adaptation (EDA) method to classify... 

    Content-Based Image Retrieval Using Relevance Feedback and Semi-Supervised Learning

    , M.Sc. Thesis Sharif University of Technology Ghasemi, Alireza (Author) ; Manzuri, Mohammad Taghi (Supervisor)
    Abstract
    Content-Based Image Retrieval has been an active research area in recent years, due to the vast amount of digital media available via the Internet. In this work we formulate contentbased image retrieval as machine learning problem using the relevance feedback technique and propose a learning algorithm adapted to specific properties of image retrieval. After studying specific properties of image retrieval as a machine learning problem, we propose a Bayesian framework for image retrieval based on one-class learning and test it on different image datasets. The proposed method is a kernel based approach and can also utilize domain knowledge in the form of prior knowledge in constructing a model... 

    Weakly Supervised Semantic Segmentation Using Deep Neural Networks

    , M.Sc. Thesis Sharif University of Technology Khairi Atani, Masoud (Author) ; Kasaei, Shohreh (Supervisor)
    Abstract
    Semantic segmentation which is the classification of every pixel in an input image is a fundamental task in the fields of computer vision and scene understanding. Applications of semantic segmentation include usage in autonomous vehicles and robotics. Since in this task dense annotation of images in the dataset is needed, recent methods have been proposed to utilize weakly-supervised and semi-supervised learning using data with weak labels and unlabeled data respectively. Because the amount of fully labeled data might not be sufficient in such methods, some papers have proposed to employ depth input data due to its rich geometrical and local information when available. In this research, an... 

    Manifold coarse graining for online semi-supervised learning

    , Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5 September 2011 through 9 September 2011 ; Volume 6911 LNAI, Issue PART 1 , September , 2011 , Pages 391-406 ; 03029743 (ISSN) ; 9783642237799 (ISBN) Farajtabar, M ; Shaban, A ; Rabiee, H. R ; Rohban, M. H ; Sharif University of Technology
    2011
    Abstract
    When the number of labeled data is not sufficient, Semi-Supervised Learning (SSL) methods utilize unlabeled data to enhance classification. Recently, many SSL methods have been developed based on the manifold assumption in a batch mode. However, when data arrive sequentially and in large quantities, both computation and storage limitations become a bottleneck. In this paper, we present a new semi-supervised coarse graining (CG) algorithm to reduce the required number of data points for preserving the manifold structure. First, an equivalent formulation of Label Propagation (LP) is derived. Then a novel spectral view of the Harmonic Solution (HS) is proposed. Finally an algorithm to reduce... 

    Incremental evolving domain adaptation

    , Article IEEE Transactions on Knowledge and Data Engineering ; Volume 28, Issue 8 , 2016 , Pages 2128-2141 ; 10414347 (ISSN) Bitarafan, A ; Soleymani Baghshah, M ; Gheisari, M ; Sharif University of Technology
    IEEE Computer Society 
    Abstract
    Almost all of the existing domain adaptation methods assume that all test data belong to a single stationary target distribution. However, in many real world applications, data arrive sequentially and the data distribution is continuously evolving. In this paper, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced. We assume that the available data for the source domain are labeled but the examples of the target domain can be unlabeled and arrive sequentially. Moreover, the distribution of the target domain can evolve continuously over time. We propose the Evolving Domain Adaptation (EDA) method that first finds a new feature space... 

    Supervised neighborhood graph construction for semi-supervised classification

    , Article Pattern Recognition ; Volume 45, Issue 4 , April , 2012 , Pages 1363-1372 ; 00313203 (ISSN) Rohban, M. H ; Rabiee, H. R ; Sharif University of Technology
    Abstract
    Graph based methods are among the most active and applicable approaches studied in semi-supervised learning. The problem of neighborhood graph construction for these methods is addressed in this paper. Neighborhood graph construction plays a key role in the quality of the classification in graph based methods. Several unsupervised graph construction methods have been proposed that have addressed issues such as data noise, geometrical properties of the underlying manifold and graph hyper-parameters selection. In contrast, in order to adapt the graph construction to the given classification task, many of the recent graph construction methods take advantage of the data labels. However, these...