Search for: dimensionality-reduction
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    The effect of a two steps searching mechanism Using Feature Vectors Related to Image Class in Improving the Performance of CBIR System

    , M.Sc. Thesis Sharif University of Technology Sherafati, Shima (Author) ; Jamzad, Mansoor (Supervisor) ; Manzuri Shalmani, Mohammad Taghi (Co-Advisor)
    Nowadays, retrieval is an inseparable part of user activities and due to growing usage of Content-Based Image Retrieval (CBIR), it has become a hot and challenging research topic specially in the past decade. The most important challenge that retrieval systems (including CBIR systems) are facing is the semantic gap between abstractions in the user’s mind and what is searched. One of the ways of dealing with this challenge is getting more information from the user about what he needs and so decreasing the distance between user’s will and what he gives to search engine as the description of his need. In this research, the class of query image is supposed to be given. For using this... 

    Feature Ranking in Text Classification

    , M.Sc. Thesis Sharif University of Technology Sadeghi, Sabereh (Author) ; Beigy, Hamid (Supervisor)
    Text classification is one if the widest and most important applications in data mining. Because of the huge number of features in these applications, a method for dimensionality reduction is needed before applying the classification algorithm. Various number of methods for dimensionality reduction and feature selection are proposed. Feature selection based on feature ranking has received much attention by researchers. The major reasons are their scalability, ease of use, and fast computation. Feature ranking methods are divided to different categories and use different measures for scoring features. Recently ensemble methods have entered the field of ranking, and achieved more accuracy... 

    Damage Detection in Offshore Jacket Structures Using Invers Vibration Problem (IVP)Technique with a Look on Probabilistic Bayesian Network Modeling

    , M.Sc. Thesis Sharif University of Technology Ahmadi, Ali (Author) ; Abbaspour, Madjid (Supervisor) ; Tabeshpour, Mohammad Reza (Supervisor)
    Damage detection in all kinds of human-made structures has been given serious attention as one of the main branches of engineering sciences, and significant progress has been made in this field, especially in the field of civil engineering, aerospace and heavy industries. However, although this topic is discussed academically in the field of offshore structures for two decades, not much development has happened in practice and it is still in the stage of knowledge development. One of the most important characteristics of practical online damage detection methods is the ability to be used knowing little information about the modal characteristics of the structure. In this research, an attempt... 

    Robust Design Optimization for Fatigue Life with Geometric and Material Uncertainties of Mechanical Parts Under Random Loading Based on Maximizing Fatigue Life and Minimizing Uncertainty in Fatigue Llife Prediction

    , M.Sc. Thesis Sharif University of Technology Esfahani, Saeed (Author) ; Khodaygan, Saeed (Supervisor)
    Fatigue life prediction of a mechanical part is one of issues which a group of engineers are engaged with it and always they try to design the parts with the maximum of lifetime. Although many researches have been done in this field but yet we can see that predicted life are different from that happens in the reality because there are some uncertainties in the phenomena. Our effort in this project is creating an algorithm design so that the parts are designed by it, have the maximum fatigue life and the minimum uncertainty in prediction. In this project we have considered geometrical, material and random loading uncertainties as error resources. Older methods those are presented in this... 

    Geometrical Fracture Modeling Within Multiple-Point Statistics Framework

    , M.Sc. Thesis Sharif University of Technology Ahmadi, Rouhollah (Author) ; Masihi, Mohsen (Supervisor) ; Rasaei, Mohammad Reza (Supervisor) ; Eskandaridalvand, Kiomars (Supervisor) ; Shahalipour, Reza (Co-Advisor)
    Majority of the oil and gas reservoirs, in the main hydrocarbon production regions around the world, are naturally fractured reservoirs. Fractures play an important role in reservoir fluid flow either in the form of high permeable complex conduits or strong permeability anisotropies. Realistic characterization of naturally fractured reservoirs requires an exhaustive understanding of fracture connectivity and fracture pattern geometry. These subsequently demand description of many fracture parameters such as density (intensity), spacing, orientation, size and aperture. Therefore, a first step in fractured reservoirs characterization is the static geometric modeling of the subsurface fracture... 

    Design and Implementation of Distributed Dimensionality Reduction Algorithms under Communication Constraints

    , M.Sc. Thesis Sharif University of Technology Rahmani, Mohammad Reza (Author) ; Maddah Ali, Mohammad Ali (Supervisor) ; Salehkaleybar, Saber (Supervisor)
    Nowadays we are witnessing the emergence of machine learning in various applications. One of the key problems in data science and machine learning is the problem of dimensionality reduction, which deals with finding a mapping that embeds samples to a lower-dimensional space such that, the relationships between the samples and their properties are preserved in the secondary space as much as possible. Obtaining such mapping is essential in today's high-dimensional settings. Moreover, due to the large volume of data and high-dimensional samples, it is infeasible or insecure to process and store all data in a single machine. As a result, we need to process data in a distributed manner.In this... 

    Using Manifold Learning for ECG Processing

    , M.Sc. Thesis Sharif University of Technology Lashgari, Elnaz (Author) ; Jahed, Mehran (Supervisor) ; Hossein Khalaj, Babak (Supervisor)
    The human heart is a complex system that contains many clues about its function in its electrocardiogram (ECG) signal. Due to the high mortality rate of heart diseases, detection and recognition of ECG arrhythmias is essential. The most difficult problem faced by ECG analysis is the vast variations among morphologies of ECG signals. In this study, we propose an approach for y detection of abnormal beats and data visualization with respect to ECG morphologies by using manifold learning. In order to do so, a nonlinear dimensionality reduction method based on the Laplacian Eigenmaps is used to reduce the high dimensions of the ECG signals, followed by the application of Bayesian and FLDA method... 

    Analysis of Designed Experiments with Multichannel Profiles Response Variable

    , M.Sc. Thesis Sharif University of Technology Badfar, Mohammad (Author) ; Niaki, Akhavan (Supervisor)
    The purpose of this research is analyzing designed experiments which their response variable is in form of multichannel profiles. For this purpose, a number of experiments with multichannel profile response variable designed at first. Then by random effect model, output data calculated. Experiments output data dimension reduced using principal component analysis and its extensions. After that, regression analysis used to analyze results of dimensionality reduction data in order to estimate coefficients of potentially effective variables in response. At the end, coefficients of effective variables classified with a hierarchical classification method in order to discover change and its root... 

    A sensitivity study of FILTERSIM algorithm when applied to DFN modeling

    , Article Journal of Petroleum Exploration and Production Technology ; Vol. 4, issue. 2 , June , 2014 , p. 153-174 ; ISSN: 21900558 Ahmadi, R ; Masihi, M ; Rasaei, M. R ; Eskandaridalvand, K ; Shahalipour, R ; Sharif University of Technology
    Realistic description of fractured reservoirs demands primarily for a comprehensive understanding of fracture networks and their geometry including various individual fracture parameters as well as network connectivities. Newly developed multiple-point geostatistical simulation methods like SIMPAT and FILTERSIM are able to model connectivity and complexity of fracture networks more effectively than traditional variogrambased methods. This approach is therefore adopted to be used in this paper. Among the multiple-point statistics algorithms, FILTERSIM has the priority of less computational effort than does SIMPAT by applying filters and modern dimensionality reduction techniques to the... 

    Optimal supervised feature extraction in internet traffic classification

    , Article IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing - Proceedings ; 2013 , Pages 102-107 ; 1555-5798 (ISSN) ; 9781479915019 (ISBN) Aliakbarian, M. S ; Fanian, A ; Saleh, F. S ; Gulliver, T. A ; Sharif University of Technology
    Internet traffic classification is important in many aspects of network management such as data exploitation detection, malicious user identification, and restricting application traffic. Previously, features such as port and protocol numbers have been used to classify traffic, but these features can now be changed easily, making their use in traffic classification inadequate. Consequently, traffic classification based on machine learning (ML) is now employed. The number of features used in an ML algorithm has a significant impact on performance, in particular accuracy. In this paper, a minimum best feature set is chosen using a supervised method to obtain uncorrelated features. Outlier... 

    A new ensemble method for feature ranking in text mining

    , Article International Journal on Artificial Intelligence Tools ; Volume 22, Issue 3 , June , 2013 ; 02182130 (ISSN) Sadeghi, S ; Beigy, H ; Sharif University of Technology
    Dimensionality reduction is a necessary task in data mining when working with high dimensional data. A type of dimensionality reduction is feature selection. Feature selection based on feature ranking has received much attention by researchers. The major reasons are its scalability, ease of use, and fast computation. Feature ranking methods can be divided into different categories and may use different measures for ranking features. Recently, ensemble methods have entered in the field of ranking and achieved more accuracy among others. Accordingly, in this paper a Heterogeneous ensemble based algorithm for feature ranking is proposed. The base ranking methods in this ensemble structure are... 

    Comparison of classification and dimensionality reduction methods used in fMRI decoding

    , Article Iranian Conference on Machine Vision and Image Processing, MVIP ; 2013 , Pages 175-179 ; 21666776 (ISSN) ; 9781467361842 (ISBN) Alamdari, N. T ; Fatemizadeh, E ; Sharif University of Technology
    In the last few years there has been growing interest in the use of functional Magnetic Resonance Imaging (fMRI) for brain mapping. To decode brain patterns in fMRI data, we need reliable and accurate classifiers. Towards this goal, we compared performance of eleven popular pattern recognition methods. Before performing pattern recognition, applying the dimensionality reduction methods can improve the classification performance; therefore, seven methods in region of interest (RDI) have been compared to answer the following question: which dimensionality reduction procedure performs best? In both tasks, in addition to measuring prediction accuracy, we estimated standard deviation of... 

    Manifold learning for ECG arrhythmia recognition

    , Article 2013 20th Iranian Conference on Biomedical Engineering, ICBME 2013 ; 2013 , Pages 126-131 Lashgari, E ; Jahed, M ; Khalaj, B ; Sharif University of Technology
    IEEE Computer Society  2013
    Heart is a complex system and we can find its function in electrocardiogram (ECG) signal. The records show high mortality rate of heart diseases. So it is essential to detect and recognize ECG arrhythmias. The problem with ECG analysis is the vast variations among morphologies of ECG signals. Premature Ventricular Contractions (PVC) is a common type of arrhythmia which may lead to critical situations and contains risk. This study, proposes a novel approach for detecting PVC and visualizing data with respect to ECG morphologies by using manifold learning. To this end, the Laplacian Eigenmaps - One of the reduction method and it is in the nonlinear category - is used to extract important... 

    Low-rank matrix approximation using point-wise operators

    , Article IEEE Transactions on Information Theory ; Volume 58, Issue 1 , September , 2012 , Pages 302-310 ; 00189448 (ISSN) Amini, A ; Karbasi, A ; Marvasti, F ; Sharif University of Technology
    The problem of extracting low-dimensional structure from high-dimensional data arises in many applications such as machine learning, statistical pattern recognition, wireless sensor networks, and data compression. If the data is restricted to a lower dimensional subspace, then simple algorithms using linear projections can find the subspace and consequently estimate its dimensionality. However, if the data lies on a low-dimensional but nonlinear space (e.g., manifolds), then its structure may be highly nonlinear and, hence, linear methods are doomed to fail. In this paper, we introduce a new technique for dimensionality reduction based on point-wise operators. More precisely, let $ {bf A} n... 

    Life-threatening arrhythmia verification in ICU patients using the joint cardiovascular dynamical model and a bayesian filter

    , Article IEEE Transactions on Biomedical Engineering ; Volume 58, Issue 10 PART 1 , 2011 , Pages 2748-2757 ; 00189294 (ISSN) Sayadi, O ; Shamsollahi, M. B ; Sharif University of Technology
    In this paper, a novel nonlinear joint dynamical model is presented, which is based on a set of coupled ordinary differential equations of motion and a Gaussian mixture model representation of pulsatile cardiovascular (CV) signals. In the proposed framework, the joint interdependences of CV signals are incorporated by assuming a unique angular frequency that controls the limit cycle of the heart rate. Moreover, the time consequence of CV signals is controlled by the same phase parameter that results in the space dimensionality reduction. These joint equations together with linear assignments to observation are further used in the Kalman filter structure for estimation and tracking. Moreover,... 

    Two-dimensional random projection

    , Article Signal Processing ; Volume 91, Issue 7 , 2011 , Pages 1589-1603 ; 01651684 (ISSN) Eftekhari, A ; Babaie-Zadeh, M ; Abrishami Moghaddam, H ; Sharif University of Technology
    As an alternative to adaptive nonlinear schemes for dimensionality reduction, linear random projection has recently proved to be a reliable means for high-dimensional data processing. Widespread application of conventional random projection in the context of image analysis is, however, mainly impeded by excessive computational and memory requirements. In this paper, a two-dimensional random projection scheme is considered as a remedy to this problem, and the associated key notion of concentration of measure is closely studied. It is then applied in the contexts of image classification and sparse image reconstruction. Finally, theoretical results are validated within a comprehensive set of... 

    Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

    , Article 2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010, 15 June 2010 through 17 June 2010 ; 2010 ; 9781424466238 (ISBN) Ramezani, M ; Heidari, A ; Fatemizadeh, E ; Soltanianzadeh, H ; Sharif University of Technology
    The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearestneighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known... 

    Nonlinear Dimensionality Reduction via Path-Based Isometric Mapping

    , Article IEEE Transactions on Pattern Analysis and Machine Intelligence ; Volume 38, Issue 7 , 2016 , Pages 1452-1464 ; 01628828 (ISSN) Najafi, A ; Joudaki, A ; Fatemizadeh, E ; Sharif University of Technology
    IEEE Computer Society 
    Nonlinear dimensionality reduction methods have demonstrated top-notch performance in many pattern recognition and image classification tasks. Despite their popularity, they suffer from highly expensive time and memory requirements, which render them inapplicable to large-scale datasets. To leverage such cases we propose a new method called "Path-Based Isomap". Similar to Isomap, we exploit geodesic paths to find the low-dimensional embedding. However, instead of preserving pairwise geodesic distances, the low-dimensional embedding is computed via a path-mapping algorithm. Due to the much fewer number of paths compared to number of data points, a significant improvement in time and memory... 

    A novel forensic image analysis tool for discovering double JPEG compression clues

    , Article Multimedia Tools and Applications ; Volume 76, Issue 6 , 2017 , Pages 7749-7783 ; 13807501 (ISSN) Taimori, A ; Razzazi, F ; Behrad, A ; Ahmadi, A ; Babaie Zadeh, M ; Sharif University of Technology
    Springer New York LLC  2017
    This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool... 

    Skin detection using contourlet texture analysis

    , Article 2009 14th International CSI Computer Conference, CSICC 2009, 20 October 2009 through 21 October 2009, Tehran ; 2009 , Pages 367-372 ; 9781424442621 (ISBN) Fotouhi, M ; Rohban, M. H ; Kasaei, S ; Sharif University of Technology
    A combined texture- and color-based skin detection is proposed in this paper. Nonsubsampled contourlet transform is used to represent texture of the whole image. Local neighbor contourlet coefficients of a pixel are used as feature vectors to classify each pixel. Dimensionality reduction is addressed through principal component analysis (PCA) to remedy the curse of dimensionality in the training phase. Before texture classification, the pixel is tested to determine whether it is skin-colored. Therefore, the classifier is learned to discriminate skin and non-skin texture for skin colored regions. A multi-layer perceptron is then trained using the feature vectors in the PCA reduced space. The...