• Ph.D. (2009)

    , Tarbiat Modares University,

  • M.Sc. (2004)

    , Tarbiat Modares University,

  • null (2002)

    , Shahid Beheshti University,

  • Machine Learning and Data mining: Sparse learning, Multi linear Learning, Deep Learning....
  • Algorithms for Large scale machine learning
  • Matrix Computation and Tensor decomposition
  • Complex Networks: Community detection, ...
  • COMLearn

    Research field:




-Associate Professor, Department of Computer Scince, Tarbiat modares university, 2018- Now -Assistant Professor, Department of Computer Science, Tarbiat Modares University, 2012-2018 Personal Webpage: http:mrezghi.ir


Curriculum Vitae (CV)

Image denoising by a novel variable‐order total fractional variation model

Fariba Kazemi Golbaghi, MR Eslahchi, Mansoor Rezghi
Journal PaperMathematical Methods in the Applied Sciences , 2021 February 24, {Pages }


The total variation model performs very well for removing noise while preserving edges. However, it gives a piecewise constant solution which often leads to the staircase effect, consequently small details such as textures are filtered out in the denoising process. Fractional‐order total variation method is one of the major approaches to overcome such drawbacks. Unlike their good quality of fractional order, all these methods use a fixed fractional order for the whole of the image. In this paper, a novel variable‐order total fractional variation model is proposed for image denoising, in which the order of fractional derivative will be allocated automatically for each pixel based on the context of the image. This kind of selection is abl

Well-to-well correlation and identifying lithological boundaries by principal component analysis of well-logs

AM Karimi, S Sadeghnejad, M Rezghi
Journal Paper , , {Pages }


A comparative study on image-based snake identification using machine learning

M Rajabizadeh, M Rezghi
Journal Paper , , {Pages }


Applying inverse stereographic projection to manifold learning and clustering

K Eybpoosh, M Rezghi, A Heydari
Journal Paper , , {Pages }


A Novel Enriched Version of Truncated Nuclear Norm Regularization for Matrix Completion of Inexact Observed Data

Tayyebeh Saeedi, Mansoor Rezghi
Journal PaperIEEE Transactions on Knowledge and Data Engineering , 2020 April 2, {Pages }


In recent years, matrix completion has become one of the main concepts in data science. Truncated nuclear norm regularization (TNNR) approximation of the rank function is an example of the favorite approaches in matrix completion that performs better than other approximations like the nuclear norm. In all TNNR based methods, the observed data is considered to be exact, so they do not give an appropriate solution for inexact observed data. But, in real applications, in addition to missing data, observed data may be inaccurate. In this paper, we proposed a novel method based on TNNR that can deal with missing data and inexact observed data by adding some terms in the objective and constraint of the matrix completion problem. Experimental resu

Extra-adaptive robust online subspace tracker for anomaly detection from streaming networks

Maryam Amoozegar, Behrouz Minaei-Bidgoli, Mansoor Rezghi, Hadi Fanaee-T
Journal PaperEngineering Applications of Artificial Intelligence , Volume 94 , 2020 September 1, {Pages 103741 }


Anomaly detection in time-evolving networks has many applications, for instance, traffic analysis in transportation networks and intrusion detection in computer networks. One group of popular methods for anomaly detection from evolving networks are robust online subspace trackers. However, these methods suffer from problem of insensitivity to drastic changes in the evolving subspace. In order to solve this problem, we propose a new robust online subspace and anomaly tracker, which is more adaptive and robust against sudden drastic changes in the subspace. More accurate estimation of low rank and sparse components by this tracker leads to more accurate anomaly detection. We evaluate the accuracy of our method with real-world dynamic network

End-to-end CNN+ LSTM deep learning approach for bearing fault diagnosis

Amin Khorram, Mohammad Khalooei, Mansoor Rezghi
Journal PaperApplied Intelligence , 2020 August 27, {Pages 16-Jan }


Fault diagnostics and prognostics are important topics both in practice and research. There is an intense pressure on industrial plants to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting and recovering potential faults in its early stages. Intelligent fault diagnosis is a promising tool due to its ability to rapidly and efficiently processing collected signals and providing accurate diagnosis results. Although many studies have developed machine leaning (M.L) and deep learning (D.L) algorithms for detecting the bearing fault, the results have generally been limited to relatively small train and test datasets and the input data has been manipulated (selective features used) to re

An adaptive synchronization approach for weights of deep reinforcement learning

S Amirreza Badran, Mansoor Rezghi
Journal PaperarXiv preprint arXiv:2008.06973 , 2020 August 16, {Pages }


Deep Q-Networks (DQN) is one of the most well-known methods of deep reinforcement learning, which uses deep learning to approximate the action-value function. Solving numerous Deep reinforcement learning challenges such as moving targets problem and the correlation between samples are the main advantages of this model. Although there have been various extensions of DQN in recent years, they all use a similar method to DQN to overcome the problem of moving targets. Despite the advantages mentioned, synchronizing the network weight in a fixed step size, independent of the agent's behavior, may in some cases cause the loss of some properly learned networks. These lost networks may lead to states with more rewards, hence better samples stored i

Generalized low-rank approximation of matrices based on multiple transformation pairs

Soheil Ahmadi, Mansoor Rezghi
Journal PaperPattern Recognition , Volume 108 , 2020 December 1, {Pages 107545 }


Dimensionality reduction is a critical step in the learning process that plays an essential role in various applications. The most popular methods for dimensionality reduction, SVD and PCA, for instance, only work on one-dimensional data. This means that for higher-order data like matrices or more generally tensors, data should be fold to the vector format. Thus, this approach ignores the spatial relationships of features and increases the probability of overfitting as well. Due to the mentioned issues, several methods like Generalized Low-Rank Approximation of Matrices (GLRAM) and Multilinear PCA (MPCA) proposed to deal with multi-dimensional data in their original format. Consequently, the spatial relationships of features preserved and t

A Hybrid Image Denoising Method Based on Integer and Fractional-Order Total Variation

Fariba Kazemi Golbaghi, Mansoor Rezghi, MR Eslahchi
Journal PaperIranian Journal of Science and Technology, Transactions A: Science , 2020 September 22, {Pages 12-Jan }


This paper introduces a new hybrid fractional model for image denoising. This proposed model is a combination of two models Rudin–Osher–Fatemi and fractional-order total variation. We try to use the advantages of two mentioned models. In this regard, after introducing an appropriate norm space, we prove the existence and uniqueness of the presented model. Furthermore, finite difference method is employed for numerically solving the obtained equation. Finally, the results illustrate the efficiency of the proposed model that yields good visual effects and a better signal-to-noise ratio.

A novel dictionary learning method based on total least squares approach with application in high dimensional biological data

Parvaneh Parvasideh, Mansoor Rezghi
Journal PaperAdvances in Data Analysis and Classification , 2020 September 2, {Pages 23-Jan }


In recent years dictionary learning has become a favorite sparse feature extraction technique. Dictionary learning represents each data as a sparse combination of atoms (columns) of the dictionary matrix. Usually, the input data is contaminated by errors that affect the quality of the obtained dictionary and so sparse features. This effect is especially critical in applications with high dimensional data such as gene expression data. Therefore, some robust dictionary learning methods have investigated. In this study, we proposed a novel robust dictionary learning algorithm, based on the total least squares, that could consider the inexactness of data in modeling. We confirm that standard and some robust dictionary learning models are the pa

A Tensor Based Framework for rs-fMRI Classification and Functional Connectivity Construction

Ali Noroozi, Mansoor Rezghi
Journal PaperFrontiers in Neuroinformatics , Volume 14 , 2020 January , {Pages 46 }


Recently machine learning methods have gained lots of publicity among researchers seeking to analyze brain images such as resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to obtain a deeper understanding of the brain and such related diseases as Alzheimer's. Finding the common patterns caused by a brain disorder through analysis of the functional connectivity(FC) network along with discriminating brain diseases from normal controls have long been the two principal goals in studying rs-fMRI data. The majority of FC extraction methods calculate the FC matrix for each subject and then use simple techniques to combine them and obtain a general functional connectivity matrix. In addition, the state-of-the-art classification techniqu

Rainfall Data Analysis of Iran using Complex Networks View

Ehsan Baratnezhad, Mansoor Rezghi
Conference Paper2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) , 2020 October 29, {Pages 175-180 }


Rainfall Zoning is one of the most significant applications in hydro-climatic science. The investigation of these regions helps us to better interpret the functional mechanism of the climatology. A popular way to detect these regions is to use a typical clustering algorithm like K-means on spatial features of the data, But it’s better to detect the zones based on the rainfall data because temporal features of rainfall data, unlike its spatial features, can cause a better result in clustering these data types. The most challenging part while using temporal data is to apply them in the presence of missing values. Here, applying a typical clustering method due to high missing values as a whole block on these data is not proper or maybe even

Even-order Toeplitz tensor: framework for multidimensional structured linear systems

Mansoor Rezghi, Maryam Amirmazlaghani
Journal PaperComputational and Applied Mathematics , Volume 38 , Issue 3, 2019 September 1, {Pages 143 }


In this paper, we introduce tensors with Toeplitz structure. These structured tensors occur in different kinds of applications such as discretization of multidimensional PDE’s or Fredholm integral equations with an invariant kernel. We investigate the main properties of the new structured tensor and show the tensor contractive product with such tenors can be carried out with the fast Fourier transform. Also, we show that approximation of Toeplitz tensors with a specially structured tensor (that will be named-product tensors) can be reduced to the rank-1 approximation of a smaller tensor. Tensor equations with such-product coefficient tenors can be solved by a direct method. So, this approximation of a Toeplitz tensor can be used to find a

Fuzzy clustering in community detection based on nonnegative matrix factorization with two novel evaluation criteria

Neda Binesh, Mansoor Rezghi
Journal PaperApplied Soft Computing , Volume 69 , 2018 August 1, {Pages 689-703 }


Clustering or community detection is one of the most important problems in social network analysis, and because of the existence of overlapping clusters, fuzzy clustering is a suitable way to cluster these networks. In fuzzy clustering, in addition to the correctness of the clusters assigned to each node, the produced membership of one node to each cluster is also important. In this paper, we introduce a new fuzzy clustering algorithm based on the nonnegative matrix factorization (NMF) method. Despite the well-known fuzzy clustering techniques like FCM, the proposed method does not depend on any parameter. Also, it can produce appropriate memberships based on the network structure and so identify the overlap nodes from non-overlap nodes, we

A novel extension of Generalized Low-Rank Approximation of Matrices based on multiple-pairs of transformations

Soheil Ahmadi, Mansoor Rezghi
Journal PaperarXiv preprint arXiv:1808.10632 , 2018 August 31, {Pages }


Dimension reduction is a main step in learning process which plays a essential role in many applications. The most popular methods in this field like SVD, PCA, and LDA, only can apply to vector data. This means that for higher order data like matrices or more generally tensors, data should be fold to a vector. By this folding, the probability of overfitting is increased and also maybe some important spatial features are ignored. Then, to tackle these issues, methods are proposed which work directly on data with their own format like GLRAM, MPCA, and MLDA. In these methods the spatial relationship among data are preserved and furthermore, the probability of overfitiing has fallen. Also the time and space complexity are less than vector-based

A splitting method for total least squares color image restoration problem

Raheleh Feiz, Mansoor Rezghi
Journal PaperJournal of Visual Communication and Image Representation , Volume 46 , 2017 July 1, {Pages 48-57 }


Color image restoration is an important problem in image processing. Using the structured total least squares (STLS) for fidelity term of the restoration process gives better results in comparison with the least squares (LS) approach. The main drawback of the STLS approach is its complexity. To overcome this issue, in this paper by an appropriate transformation the color image restoration is substituted with two smaller subproblems corresponding to smooth and oscillatory parts of the image. The first and second subproblems are modeled via STLS and LS approaches, respectively. We show that the proposed method is faster than STLS and gives competitive solutions with it. Also, we demonstrate that Haar wavelet perseveres the structure of the bl


Journal Paper , Volume 3 , Issue 1, 2017 January 1, {Pages 25-36 }


Linear dimension reduction has been used in different application such as image processing and pattern recognition. All these data folds the original data to vectors and project them to an small dimensions. But in some applications such we may face with data that are not vectors such as image data. Folding the multidimensional data to vectors causes curse of dimensionality and mixed the different feature together. For solving this problem in recent years some multilinear methods have been proposed. beside vector modeling that problem becomes finding the eigenvalues of matrices, in mullinear viewpoint the problem has not such analytical meaning and should be solved by optimization techniques. In this paper by reviewing a new multi linear DAT

A novel fast tensor-based preconditioner for image restoration

Mansoor Rezghi
Journal PaperIEEE Transactions on Image Processing , Volume 26 , Issue 9, 2017 September , {Pages 4499-4508 }


Image restoration is one of the main parts of image processing. Mathematically, this problem can be modeled as a large-scale structured ill-posed linear system. Ill-posedness of this problem results in low-convergence rate of iterative solvers. For speeding up the convergence, preconditioning usually is used. Despite the existing preconditioners for image restoration, which are constructed based on approximations of the blurring matrix, in this paper, we propose a novel preconditioner with a different viewpoint. Here, we show that image restoration problem can be modeled as a tensor contractive linear equation. This modeling enables us to propose a new preconditioner based on an approximation of the blurring tensor operator. Due to the part

Improving image segmentation by using energy function based on mixture of Gaussian pre-processing

Nima Vakili, Mansoor Rezghi, S Mohammad Hosseini
Journal PaperJournal of Visual Communication and Image Representation , Volume 41 , 2016 November 1, {Pages 239-246 }


In this paper, by proposing a two-stage segmentation method based on active contour model, we improve the procedure of former image segmentation methods. The first stage of our method is computing weights, means and variances of image by utilizing Mixture of Gaussian distribution which parameters are obtained from EM-algorithm. Once they are obtained, in the second stage, by incorporating level set method for minimizing energy function, the segmentation is achieved. We use an adaptive direction function to make the curve evolution robust against the curves initial position and a nonlinear adaptive velocity to speed up the process of curve evolution and also a probability-weighted edge and region indicator function to implement a robust segm

Current Teaching

  • MS.c.

    Deep Learing

  • MS.c.

    Special Topics in Artificial intelligence

Teaching History

  • MS.c.

    Complex Networks

  • MS.c.

    Machine learning

  • 2020
    Tabatabaei mortazavi, Soheil
    Designing a novel deep multi-stage generative adversarial network (Deep Multi-Stage GAN)
  • 2020
    Dehghani Firoozabadi, Mehdi
  • 2021
    Mahmoudabadi, Mohamad
  • 2021
    Nemati Andavari, Mitra



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