A review of feature selection and its methods
B Venkatesh, J Anuradha - Cybernetics and information technologies, 2019 - sciendo.com
Nowadays, being in digital era the data generated by various applications are increasing
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …
A comprehensive survey on the process, methods, evaluation, and challenges of feature selection
Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …
Hessian-based semi-supervised feature selection using generalized uncorrelated constraint
Feature selection (FS) aims to eliminate redundant features and choose the informative
ones. Since labeled data are not always easily available and abundant unlabeled data are …
ones. Since labeled data are not always easily available and abundant unlabeled data are …
A survey on semi-supervised feature selection methods
Feature selection is a significant task in data mining and machine learning applications
which eliminates irrelevant and redundant features and improves learning performance. In …
which eliminates irrelevant and redundant features and improves learning performance. In …
An adaptive semisupervised feature analysis for video semantic recognition
Video semantic recognition usually suffers from the curse of dimensionality and the absence
of enough high-quality labeled instances, thus semisupervised feature selection gains …
of enough high-quality labeled instances, thus semisupervised feature selection gains …
Top-k Feature Selection Framework Using Robust 0–1 Integer Programming
Feature selection (FS), which identifies the relevant features in a data set to facilitate
subsequent data analysis, is a fundamental problem in machine learning and has been …
subsequent data analysis, is a fundamental problem in machine learning and has been …
Multi-pie
A close relationship exists between the advancement of face recognition algorithms and the
availability of face databases varying factors that affect facial appearance in a controlled …
availability of face databases varying factors that affect facial appearance in a controlled …
Robust structured subspace learning for data representation
To uncover an appropriate latent subspace for data representation, in this paper we propose
a novel Robust Structured Subspace Learning (RSSL) algorithm by integrating image …
a novel Robust Structured Subspace Learning (RSSL) algorithm by integrating image …
A convex formulation for semi-supervised multi-label feature selection
Explosive growth of multimedia data has brought challenge of how to efficiently browse,
retrieve and organize these data. Under this circumstance, different approaches have been …
retrieve and organize these data. Under this circumstance, different approaches have been …
Semisupervised feature analysis by mining correlations among multiple tasks
In this paper, we propose a novel semisupervised feature selection framework by mining
correlations among multiple tasks and apply it to different multimedia applications. Instead of …
correlations among multiple tasks and apply it to different multimedia applications. Instead of …