Ashkan Panahi

Works (8)

Updated: March 13th, 2024 08:24

2021 journal article

Dynamic Graph Learning: A Structure-Driven Approach

MATHEMATICS, 9(2).

By: B. Jiang n, Y. Huang n, A. Panahi*, Y. Yu *, H. Krim n & S. Smith *

author keywords: dynamic graph learning; graph signal processing; sparse signal; convex optimization
TL;DR: The purpose of this paper is to infer a dynamic graph as a global model of time-varying measurements at a set of network nodes, which captures both pairwise as well as higher order interactions among the nodes. (via Semantic Scholar)
Source: Web Of Science
Added: March 15, 2021

2020 journal article

Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

IEEE SENSORS JOURNAL, 20(20), 12307–12316.

By: S. Ghanem n, A. Panahi n, H. Krim n & R. Kerekes *

author keywords: Sensor fusion; Data integration; Data models; Sparse matrices; Magnetic sensors; Sensor phenomena and characterization; Sparse learning; unsupervised classification; data fusion; multimodal data
TL;DR: The resulting fusion of the unlabeled sensors’ data from experiments on audio and magnetic data has shown that the method is competitive with other state of the art subspace clustering methods. (via Semantic Scholar)
Source: Web Of Science
Added: October 12, 2020

2019 journal article

Analysis Dictionary Learning Based Classification: Structure for Robustness

IEEE TRANSACTIONS ON IMAGE PROCESSING, 28(12), 6035–6046.

By: W. Tang n, A. Panahi n, H. Krim  n & L. Dai*

author keywords: Discriminate analysis dictionary learning; distributed analysis dictionary learning; structured mapping; supervised learning
TL;DR: A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
10. Reduced Inequalities (OpenAlex)
Source: Web Of Science
Added: October 19, 2020

2019 journal article

Community Detection and Improved Detectability in Multiplex Networks

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 7(3), 1697–1709.

By: Y. Huang n, A. Panahi*, H. Krim n & L. Dai*

author keywords: Multiplexing; Stochastic processes; Belief propagation; Correlation; Periodic structures; Computational modeling; Bayes methods; Network theory (graphs); graphical models; belief propagation
TL;DR: A generative model that leverages the multiplicity of a single community in multiple layers, with no prior assumption on the relation of communities among different layers is proposed, which shows a better detection performance over a certain correlation and signal to noise ratio (SNR) range. (via Semantic Scholar)
Source: Web Of Science
Added: September 21, 2020

2019 journal article

Deep Dictionary Learning: A PARametric NETwork Approach

IEEE TRANSACTIONS ON IMAGE PROCESSING, 28(10), 4790–4802.

By: S. Mahdizadehaghdam n, A. Panahi n, H. Krim n & L. Dai*

author keywords: Image classification; deep learning; sparse representation
TL;DR: The performance of the proposed hierarchical method increases by adding more layers, which consequently makes this model easier to tune and adapt and shows a remarkably lower fooling rate in the presence of adversarial perturbation. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: Web Of Science
Added: August 26, 2019

2019 article

Sparse Generative Adversarial Network

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW), pp. 3063–3071.

By: S. Mahdizadehaghdam n, A. Panahi n & H. Krim n

TL;DR: A new approach to Generative Adversarial Networks (GANs) to achieve an improved performance with additional robustness to its so-called and well-recognized mode collapse, by mapping the desired data onto a frame-based space for a sparse representation to lift any limitation of small support features prior to learning the structure. (via Semantic Scholar)
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Source: Web Of Science
Added: September 7, 2020

2018 article

Robust Subspace Clustering by Bi-sparsity Pursuit: Guarantees and Sequential Algorithm

2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018), pp. 1302–1311.

By: A. Panahi n, X. Bian  n, L. Krim  n & L. Dai *

TL;DR: This work considers subspace clustering under sparse noise, and provides an analysis of this optimization problem demonstrating that this approach is capable of recovering linear subspaces as a local optimal solution for sufficiently large data sets and sparse noise vectors. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2017 journal article

Performance Analysis of Sparsity-Based Parameter Estimation

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 65(24), 6478–6488.

By: A. Panahi n & M. Viberg  n

author keywords: Superresolution theory; performance bounds; error analysis; LASSO; atomic norm regularization; atomic decomposition; continuous LASSO; off-grid estimation
TL;DR: A novel analysis of the LASSO as an estimator of continuous parameters by providing a novel framework for the analysis by studying nearly ideal sparse solutions and quantifying the error in the high signal-to-noise ratio regime. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

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