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Found 8 papers, 0 papers with code
Detecting adversaries in Crowdsourcing
Despite its successes in various machine learning and data science tasks, crowdsourcing can be susceptible to attacks from dedicated adversaries.
Bayesian Crowdsourcing with Constraints
Crowdsourcing has emerged as a powerful paradigm for efficiently labeling large datasets and performing various learning tasks, by leveraging crowds of human annotators.
Unsupervised Ensemble Classification with Sequential and Networked Data
data, the present work introduces an unsupervised scheme for learning from ensembles of classifiers in the presence of data dependencies.
Nonlinear Dimensionality Reduction on Graphs
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.
Blind Multiclass Ensemble Classification
The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools.
Sketched Subspace Clustering
The immense amount of daily generated and communicated data presents unique challenges in their processing.
Large-scale subspace clustering using sketching and validation
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.
Sketch and Validate for Big Data Clustering
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.
