Search Results for author: Marianna Pensky

Found 13 papers, 0 papers with code

Signed Diverse Multiplex Networks: Clustering and Inference

no code implementations14 Feb 2024 Marianna Pensky

The paper introduces a Signed Generalized Random Dot Product Graph (SGRDPG) model, which is a variant of the Generalized Random Dot Product Graph (GRDPG), where, in addition, edges can be positive or negative.

Clustering

Sparse Subspace Clustering in Diverse Multiplex Network Model

no code implementations15 Jun 2022 Majid Noroozi, Marianna Pensky

The DIMPLE model generalizes a multitude of papers that study multilayer networks with the same community structures in all layers, as well as the Mixture Multilayer Stochastic Block Model (MMLSBM), where the layers in the same group have identical matrices of block connection probabilities.

Clustering Stochastic Block Model

ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network

no code implementations20 Feb 2021 Xing Fan, Marianna Pensky, Feng Yu, Teng Zhang

The paper considers a Mixture Multilayer Stochastic Block Model (MMLSBM), where layers can be partitioned into groups of similar networks, and networks in each group are equipped with a distinct Stochastic Block Model.

Clustering Stochastic Block Model +1

The Hierarchy of Block Models

no code implementations7 Feb 2020 Majid Noroozi, Marianna Pensky

There exist various types of network block models such as the Stochastic Block Model (SBM), the Degree Corrected Block Model (DCBM), and the Popularity Adjusted Block Model (PABM).

Clustering Stochastic Block Model

Sparse Popularity Adjusted Stochastic Block Model

no code implementations3 Oct 2019 Majid Noroozi, Marianna Pensky, Ramchandra Rimal

In the present paper we study a sparse stochastic network enabled with a block structure.

Stochastic Block Model

Estimation and Clustering in Popularity Adjusted Stochastic Block Model

no code implementations1 Feb 2019 Majid Noroozi, Ramchandra Rimal, Marianna Pensky

The paper considers the Popularity Adjusted Block model (PABM) introduced by Sengupta and Chen (2018).

Statistics Theory Statistics Theory 62F12, 62H30

Sparse One-Time Grab Sampling of Inliers

no code implementations21 Dec 2018 Maryam Jaberi, Marianna Pensky, Hassan Foroosh

One of the main approaches that is explored in the literature to tackle the problems of size and dimensionality is sampling subsets of the data in order to estimate the characteristics of the whole population, e. g. estimating the underlying clusters or structures in the data.

Clustering

Probabilistic Sparse Subspace Clustering Using Delayed Association

no code implementations28 Aug 2018 Maryam Jaberi, Marianna Pensky, Hassan Foroosh

(ii) We demonstrate that delayed association is better suited for clustering subspaces that have ambiguities, i. e. when subspaces intersect or data are contaminated with outliers/noise.

Clustering

Spectral clustering in the dynamic stochastic block model

no code implementations2 May 2017 Marianna Pensky, Teng Zhang

We estimate the edge probability tensor by a kernel-type procedure and extract the group memberships of the nodes by spectral clustering.

Clustering Stochastic Block Model

Solution of linear ill-posed problems using random dictionaries

no code implementations25 May 2016 Pawan Gupta, Marianna Pensky

In the present paper we consider application of overcomplete dictionaries to solution of general ill-posed linear inverse problems.

Classification with many classes: challenges and pluses

no code implementations4 Jun 2015 Felix Abramovich, Marianna Pensky

The objective of the paper is to study accuracy of multi-class classification in high-dimensional setting, where the number of classes is also large ("large $L$, large $p$, small $n$" model).

Classification feature selection +2

SWIFT: Sparse Withdrawal of Inliers in a First Trial

no code implementations CVPR 2015 Maryam Jaberi, Marianna Pensky, Hassan Foroosh

We study the simultaneous detection of multiple structures in the presence of overwhelming number of outliers in a large population of points.

Clustering

Sparse Convolutional Neural Networks

no code implementations CVPR 2015 Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, Marianna Pensky

Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity.

Image Classification object-detection +1

Cannot find the paper you are looking for? You can Submit a new open access paper.