no code implementations • 7 Oct 2021 • 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.
no code implementations • 20 Dec 2020 • 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.
no code implementations • 22 Jun 2019 • Panagiotis A. Traganitis, Georgios B. Giannakis
data, the present work introduces an unsupervised scheme for learning from ensembles of classifiers in the presence of data dependencies.
no code implementations • 29 Jan 2018 • Yanning Shen, Panagiotis A. Traganitis, Georgios B. Giannakis
The novel framework encompasses most of the existing dimensionality reduction methods, but it is also capable of capturing and preserving possibly nonlinear correlations that are ignored by linear methods.
no code implementations • 8 Dec 2017 • 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.
no code implementations • 22 Jul 2017 • Panagiotis A. Traganitis, Georgios B. Giannakis
The immense amount of daily generated and communicated data presents unique challenges in their processing.
no code implementations • 6 Oct 2015 • Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis
At the heart of SkeVa-SC lies a randomized scheme for approximating the underlying probability density function of the observed data by kernel smoothing arguments.
no code implementations • 22 Jan 2015 • 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) data.