60 citations · 127 across the 7 of their papers we have counts for
10 papers
Subspace Projection Methods for Fast Spectral Embeddings of Evolving Graphs
Mohammad Eini, Abdullah Karaaslanli, Vassilis Kalantzis +1
Several graph data mining, signal processing, and machine learning downstream tasks rely on information related to the eigenvectors of the associated adjacency or Laplacian matrix.…
Learning From Crowdsourced Noisy Labels: A Signal Processing Perspective
Shahana Ibrahim, Panagiotis A. Traganitis, Xiao Fu +1
One of the primary catalysts fueling advances in artificial intelligence (AI) and machine learning (ML) is the availability of massive, curated datasets. A commonly used technique…
Detecting adversaries in Crowdsourcing
Panagiotis A. Traganitis, Georgios B. Giannakis
Despite its successes in various machine learning and data science tasks, crowdsourcing can be susceptible to attacks from dedicated adversaries. This work investigates the effects…
Bayesian Crowdsourcing with Constraints
Panagiotis A. Traganitis, Georgios B. Giannakis
Crowdsourcing has emerged as a powerful paradigm for efficiently labeling large datasets and performing various learning tasks, by leveraging crowds of human annotators. When addit…
Unsupervised Ensemble Classification with Sequential and Networked Data
Panagiotis A. Traganitis, Georgios B. Giannakis
Ensemble learning, the machine learning paradigm where multiple algorithms are combined, has exhibited promising perfomance in a variety of tasks. The present work focuses on unsup…
Nonlinear Dimensionality Reduction on Graphs
Yanning Shen, Panagiotis A. Traganitis, Georgios B. Giannakis
In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated f…
Blind Multiclass Ensemble Classification
Panagiotis A. Traganitis, Alba Pagès-Zamora, Georgios B. Giannakis
The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools. However, as each algorithm has its st…
Sketched Subspace Clustering
Panagiotis A. Traganitis, Georgios B. Giannakis
The immense amount of daily generated and communicated data presents unique challenges in their processing. Clustering, the grouping of data without the presence of ground-truth la…
Large-scale subspace clustering using sketching and validation
Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis
The nowadays massive amounts of generated and communicated data present major challenges in their processing. While capable of successfully classifying nonlinearly separable object…
Sketch and Validate for Big Data Clustering
Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis
In response to the need for learning tools tuned to big data analytics, the present paper introduces a framework for efficient clustering of huge sets of (possibly high-dimensional…