Unsupervised Feature Selection Based on Kernel Fisher Discriminant Analysis and Regression Learning

Machine Learning - Netherlands
doi 10.1007/s10994-018-5765-6
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Springer Science and Business Media LLC

Related search

Feature Selection and Kernel Learning for Local Learning-Based Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern RecognitionApplied MathematicsComputer VisionMathematicsComputational TheoryArtificial IntelligenceSoftware
2011English

Classifier-Independent Feature Selection Based on Non-Parametric Discriminant Analysis

Lecture Notes in Computer Science
Computer ScienceTheoretical Computer Science
2002English

Unsupervised Wishart Classfication of Wetlands in Newfoundland, Canada Using Polsar Data Based on Fisher Linear Discriminant Analysis

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
2016English

Daily Human Physical Activity Recognition Based on Kernel Discriminant Analysis and Extreme Learning Machine

Mathematical Problems in Engineering
MathematicsEngineering
2015English

Unsupervised Feature Selection on Data Streams

2015English

Semi-Supervised Feature Selection With Adaptive Discriminant Analysis

Proceedings of the AAAI Conference on Artificial Intelligence
2019English

Optimal Feature Selection in High-Dimensional Discriminant Analysis

IEEE Transactions on Information Theory
Computer Science ApplicationsInformation SystemsLibraryInformation Sciences
2015English

Unsupervised Feature Learning Assisted Visual Sentiment Analysis

International Journal of Multimedia and Ubiquitous Engineering
Computer Science
2016English

A New Unsupervised Feature Selection Algorithm Using Similarity-Based Feature Clustering

Computational Intelligence
Computational MathematicsArtificial Intelligence
2019English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy