An Unsupervised Online Streaming Feature Selection Algorithm with Density Peak Clustering-Reference-Cited by-同舟云学术

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An Unsupervised Online Streaming Feature Selection Algorithm with Density Peak Clustering

Published:2023-10-25 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Networking, Sensing and Control (ICNSC)
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Author:

Song Yu¹,Liu Zhigang²

Affiliation:

1. Chongqing University of Posts and Telecommunications Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences Chongqing School, University of Chinese Academy of Sciences,School of Computer Science and Technology,Chongqing,China

2. Dongguan University of Technology,School of Computer Science and Technology,Dongguan,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10318842/10318843/10319011.pdf?arnumber=10319011

Reference42 articles.

1. Online Feature Selection for Streaming Features Using Self-Adaption Sliding-Window Sampling

2. A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection

3. Scalable and Accurate Online Feature Selection for Big Data

4. A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

5. Unsupervised Feature Selection with Feature Clustering

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