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graphic-model
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Structure learning of sparse GGMS over multiple access networks
, Article IEEE Transactions on Communications ; Volume 68, Issue 2 , 2020 , Pages 987-997 ; Karamzade, A ; Mirzaeifard, R ; Motahari, S. A ; Manzuri Shalmani, M. T ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from a dataset distributed across multiple local machines. The local machines can communicate with the central machine through a wireless multiple access channel. In this paper, we are interested in designing effective strategies where reliable learning is feasible under power and bandwidth limitations. Two approaches are proposed: Signs and Uncoded methods. In the Signs method, the local machines quantize their data into binary vectors and an optimal channel coding scheme is used to reliably send the vectors to the central machine where the structure is learned from the received...
Inference in Graphical Models
, M.Sc. Thesis Sharif University of Technology ; Alishahi, Kasra (Supervisor) ; Haji Mirsadeghi, Mir Omid (Supervisor)
Abstract
The purpose of this dissertation is to study issues in the field of graphical models.At the beginning, we will mention the main concepts of graphical models. Then we describe algorithms in exact inference. These algorithms are used to solve inferential issues and when the graph is related to the tree graph modeling. We also describe how these algorithms apply to non-tree graphs. In addition, we recall definitions such as cumulative function and set of mean parameters and important theorems applied in graphical models. Finally, we describe the important algorithms that are used to estimate the parameters in graphical models
A top down approach to semi-structured database design
, Article 2nd International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2009 ; 2009 , Pages 26-31 ; 9781424444571 (ISBN) ; Mirian Hosseinabadi, S. H ; Sharif University of Technology
Abstract
XML has become the preferred format for representing and exchanging structured and semi-structured data on the web. The XML Schema language is widely used for defining and validating highly structured XML instance documents. While text-based languages, such as XML-Schema, offer great advantages for data interchange on the Internet, graphical modeling languages are widely accepted as a more visually effective means of specifying and communicating data requirements for a human audience. This paper uses Object-Relationship-Attribute model (ORA-SS) as a conceptual graphical model for designing XML-Schemas. To facilitate this process we introduce an xml representation for ORA-SS Schema Diagram...
Stratification of admixture population:A bayesian approach
, Article 7th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2019, 29 January 2019 through 31 January 2019 ; 2019 ; 9781728106731 (ISBN) ; Taheri, S. M ; Motahari, S. A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
A statistical algorithm is introduced to improve the false inference of active loci, in the population in which members are admixture. The algorithm uses an advanced clustering algorithm based on a Bayesian approach. The proposed algorithm simultaneously infers the hidden structure of the population. In this regard, the Monte Carlo Markov Chain (MCMC) algorithm has been used to evaluate the posterior probability distribution of the model parameters. The proposed algorithm is implemented in a bundle, and then its performance is widely evaluated in a number of artificial databases. The accuracy of the clustering algorithm is compared with the STRUCTURE method based on certain criterion. © 2019...
Learning of tree-structured Gaussian graphical models on distributed data under communication constraints
, Article IEEE Transactions on Signal Processing ; Volume 67, Issue 1 , 2019 , Pages 17-28 ; 1053587X (ISSN) ; Motahari, S. A ; Manzuri Shalmani, M. T ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our...
Learning of tree-structured gaussian graphical models on distributed data under communication constraints
, Article IEEE Transactions on Signal Processing ; Volume 67, Issue 1 , 2019 , Pages 17-28 ; 1053587X (ISSN) ; Motahari, A ; Manzuri Shalmani, M. T ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our...
Learning of tree-structured Gaussian graphical models on distributed data under communication constraints
, Article IEEE Transactions on Signal Processing ; Volume 67, Issue 1 , 2019 , Pages 17-28 ; 1053587X (ISSN) ; Motahari, S. A ; Manzuri Shalmani, M. T ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our...
Hyperspectral Unmixing using Structured Sparse Representation
, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor)
Abstract
Hyperspectral imaging is one of the remote sensing methods that has been widely applied in different applications. A hyperspectral image is composed of a set of pixels showing the spectral signatures in different frequency bands recorded by sensor cells. The process that detects the proportion of pure elements in the combination of pixels is called hyperspectral unmixing. Noisy and incomplete data, high mutual coherence of spectral libraries and different sensor settings are some challenges of the unmixing problem. In this work, we focus on semi-supervised linear hyperspectral unmixing in which a spectral library is given. The resulting linear equation is an underdetermind problem with...
Probabilistic Reasoning in Collaborative Filtering
, M.Sc. Thesis Sharif University of Technology ; Izadi, Mohammad (Supervisor)
Abstract
In this thesis the usage of probabilistic reasoning in collaborative filtering is investigated. The problem of predicting users' rating is formulated as a Bayesian decision problem and a generative probabilistic model is used in order to find the optimal decision. Two different probabilistic models are considered: user based model and rating based model. In user based model prediction of ratings is based on structural learning of Bayesian networks. In rating based model, we assume a predefined Bayesian network represents the joint distribution over model variables and rating prediction is carried out using McMc inference method. MovieLens dataset is chosen to evaluate and compare the results...
Distributed Structure Learning of Gaussian Graphical Models
, M.Sc. Thesis Sharif University of Technology ; Manzuri Shalmani, Mohammad-Taghi (Supervisor) ; Motahari, Abolfazl (Co-Supervisor)
Abstract
Nowadays, the explosion of the volume data provides more accuracy in machine learning models. But, Working with a vast amount of data is not easy, especially in a situation that data are distributed over the systems. In such systems, designing distributed learning algorithms that in communication efficient setting demand reliable and more accurate results, are so important. We studied sparse structure learning of Gaussian graphical model in a situation that our data are distributed over the system and each machine has a dimension of data. Each local machine should send its data to a central machine and the central machine is responsible for learning the structure. For reliable learning under...
Human Action Recognition Using Expandable Graphical Models
, M.Sc. Thesis Sharif University of Technology ; Kasaei, Shohreh (Supervisor)
Abstract
In recent years, ability of computers to recognize human actions, because of numerousapplications, has attracted scientists. Surveillancesystems in house, work and public places, human computer interaction, study of human movement problems, remote supervision of ill or old people and sport training are only some of the applications. In this thesis 10 actions are considered. These actions are Walking, Running, Galloping side, Bending, Jump jacking, Jumping, Jumping in place, Skipping, Waving one hand and Waving two hands. All actions exist in Weisemann dataset so this dataset is used as training and testing dataset. Here important objectives are recognising human action so that it is...
Improving Sampling Efficiency of Probabilistic Graphical Models
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Deep learning methods have become more popular in the past years. These methods use complex network architectures to model rich, hierarchical datasets. Although most of the research has been centered around Discriminative models, however, recently a lot of research is focused on Deep Generative Models. Two of the pioneering models in this field are Generative Adversarial Networks and Variational Auto-Encoders. In addition, knowing the structure of data helps models to search in a narrower hypothesis space. Most of the structure in datasets are models using Probabilistic Graphical Models. Using this structural information, one can achieve better parameter estimations. In the case of...
Distributed Machine Learning with Communication Constraints
, Ph.D. Dissertation Sharif University of Technology ; Manzuri Shalmani, Mohammad Taghi (Supervisor) ; Motahari, Abolfazl (Supervisor)
Abstract
It is of fundamental importance to find algorithms obtaining optimal performance for learning of statistical models in distributed and communication limited systems. In this thesis, we aim at characterizing the best learning strategies over distributed datasets such that the communications between storing machines are minimized. We have addressed two problems in distributed setting: learning of Gaussian processes, and structure learning of Gaussian Graphical Models (GGM). The performance of the proposed methods are analyzed theoritically and verified experimentally. The experimental results show that with spending few bits the proposed distributed methods have close performance to the...
Structure Learning From Distributed Noisy Data
, M.Sc. Thesis Sharif University of Technology ; Motahari, Abolfazl (Supervisor) ; Manzuri Shalmani, Mohammad Taghi (Co-Supervisor)
Abstract
Probabilistic graphical models have great applications in studying and analyzing realworld data. For instance, these models have been used in reconstructing gene regularity networks. Specifically, learning the edges’ structure of graphical models is of great importance.Knowledge about the underlying structure of a graphical model brings about a valuable framework for the decomposition of the model’s distribution and reveals important information such as dependency among dimensions of samples, etc. Most existing methods for structure learning obtain the underlying structure of the model in a centralized fashion and without considering noise in data. In many applications, data exist in a...
Viral cascade probability estimation and maximization in diffusion networks
, Article IEEE Transactions on Knowledge and Data Engineering ; 28 May , 2018 ; 10414347 (ISSN) ; Beigy, H ; Sharif University of Technology
IEEE Computer Society
2018
Abstract
People use social networks to share millions of stories every day, but these stories rarely become viral. Can we estimate the probability that a story becomes a viral cascade If so, can we find a set of users that are more likely to trigger viral cascades These estimation and maximization problems are very challenging since both rare-event nature of viral cascades and efficiency requirement should be considered. Unfortunately, this problem still remains largely unexplored to date. In this paper, given temporal dynamics of a network, we first develop an efficient viral cascade probability estimation method, VICE, that leverages an special importance sampling approximation to achieve high...
3D Reconstruction of Human Pose in Multi-View Dynamic Scenes
, Ph.D. Dissertation Sharif University of Technology ; Sanaei, Esmaeil (Supervisor) ; Kasaei, Shohreh (Co-Advisor)
Abstract
In this thesis, 3D pose reconstruction of one or multiple humans in multi-view dynamic scene is considered. Inputs are multi-view frames of multi-view camera systems. Outputs are the 3D reconstructed human poses in 3D space. The pose is the location of 14 human body joints in the 3D space. In this research, it is not allowed to use Kinect sensor data or other Markers or GPS sensors. It is supposed that only multi-view images are used. 3D reconstruction of human body pose can be performed with different assumptions. For example, the scene is indoor only or the camera calibration information is provided at first.Cameras are moving or fixed. In this research, each of this assumption is regarded...
Management of Classifiers Pool in Data Stream Classification Using Probabilistic Graphical Models
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Concept drift is a common situation in data streams where distribution which data is generated from, changes over time due to various reasons like environmental changes. This phenomenon challenges classification process strongly. Recent studies on keeping a pool of classifiers each modeling one of the concepts, have achieved promising results. Storing used classifiers in a pool enables us to exploit prior knowledge of concepts in the future occurrence of them. Most of the methods presented so far, introduce a similarity measure between current and past concepts and select the closest stored concept as current one. These methods don’t consider possible relations and dependenies between...
Analysing Purchase Satisfaction Using Opinion Mining
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Opinions and experiences of others give us valuable information in making decisions. Recently, with the expansion of using social networks and websites, people can easily share their opinions about miscellaneous things. This huge amount of information cannot be analyzed by individuals, so a system that automatically analyzes opinions is needed. This need invokes new field of research that is called opinion mining. User’s viewpoints could change during the time, and this is an important issue for companies. One of the most challenging sub-problems of opinion mining is model-based opinion mining, which aims to model the generation of words by modeling their probabilities. In this thesis, we...
Effective Connectivity Analysis in Neural circuitry Underlying Perceptual and Value-based Memory
, M.Sc. Thesis Sharif University of Technology ; Amini, Arash (Supervisor) ; Ghazizadeh, Ali (Co-Supervisor)
Abstract
Perceptual memory used in novel vs familiar discrimination is not only vital for the evaluation of environmental variations but also essential for learning, perception, and correcting behavioral policies. On the other hand, value-based memory which allows for discrimination of valuable objects among equally familiar ones also drives behavioral interactions and decision making. Although many studies have been conducted to address the neuronal association regarding each separately, the neural correspondence between perceptual and value-based memory is not scrutinized adequately. To this end, the differential neural activation in two macaque monkeys to unrewarded novel vs familiar fractals (>100...
Learning Deep Generative Models for Structured Data
,
Ph.D. Dissertation
Sharif University of Technology
;
Beigy, Hamid
(Supervisor)
Abstract
Recently, a new generation of machine learning tasks, namely data generation, was born by emerging deep networks and modern methods for training neural networks on one hand, and the growth of available training data for training these networks on the other hand. Although distribution estimation and sampling were well-known problems in the science of statics, deep generative models can properly generate samples from real world distributions that common statistical methods fail in them e.g., image and music generation.Due to these improvements in deep generative models, researchers have recently tried to propose deep generative models for datasets with complex structures. These structured...