Search for: dimensionality-reduction
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    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... 

    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... 

    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... 

    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... 

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

    , Article IEEE Transactions on Knowledge and Data Engineering ; 2018 ; 10414347 (ISSN) Hosseini Akbarnejad, A ; Soleymani Baghshah, M ; Sharif University of Technology
    IEEE Computer Society  2018
    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... 

    Private Inner product retrieval for distributed machine learning

    , Article 2019 IEEE International Symposium on Information Theory, ISIT 2019, 7 July 2019 through 12 July 2019 ; Volume 2019-July , 2019 , Pages 355-359 ; 21578095 (ISSN); 9781538692912 (ISBN) Mousavi, M. H ; Maddah Ali, M. A ; Mirmohseni, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    In this paper, we argue that in many basic algorithms for machine learning, including support vector machine (SVM) for classification, principal component analysis (PCA) for dimensionality reduction, and regression for dependency estimation, we need the inner products of the data samples, rather than the data samples themselves.Motivated by the above observation, we introduce the problem of private inner product retrieval for distributed machine learning, where we have a system including a database of some files, duplicated across some non-colluding servers. A user intends to retrieve a subset of specific size of the set of the inner product of every pair of data items in the database with... 

    Ordinal embedding: Approximation algorithms and dimensionality reduction

    , Article 11th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2008 and 12th International Workshop on Randomization and Computation, RANDOM 2008, Boston, MA, 25 August 2008 through 27 August 2008 ; Volume 5171 LNCS , 2008 , Pages 21-34 ; 03029743 (ISSN) ; 9783540853626 (ISBN) Bǎdoiu, M ; Demaine, E. D ; Hajiaghayi, M ; Sidiropoulos, A ; Zadimoghaddam, M ; Sharif University of Technology
    This paper studies how to optimally embed a general metric, represented by a graph, into a target space while preserving the relative magnitudes of most distances. More precisely, in an ordinal embedding, we must preserve the relative order between pairs of distances (which pairs are larger or smaller), and not necessarily the values of the distances themselves. The relaxation of an ordinal embedding is the maximum ratio between two distances whose relative order is inverted by the embedding. We develop polynomial-time constant-factor approximation algorithms for minimizing the relaxation in an embedding of an unweighted graph into a line metric and into a tree metric. These two basic target... 

    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... 

    Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series

    , Article Biomedical Physics and Engineering Express ; Volume 6, Issue 5 , 2020 Ahmadi, H ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    IOP Publishing Ltd  2020
    Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three... 

    An exploratory study to design a novel hand movement identification system

    , Article Computers in Biology and Medicine ; Volume 39, Issue 5 , 2009 , Pages 433-442 ; 00104825 (ISSN) Khezri, M ; Jahed, M ; Sharif University of Technology
    Electromyogram signal (EMG) is an electrical manifestation of contractions of muscles. Surface EMG (sEMG) signal collected from the surface of skin has been used in diverse applications. One of its usages is in pattern recognition of hand prosthesis movements. The ability of current prosthesis devices has been generally limited to simple opening and closing tasks, minimizing their efficacy compared to natural hand capabilities. In order to extend the abilities and accuracy of prosthesis arm movements and performance, a novel sEMG pattern recognizing system is proposed. To extract more pertinent information we extracted sEMGs for selected hand movements. These features constitute our main... 

    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,...