TUMK-ELM: A Fast Unsupervised Heterogeneous Data Learning Approach

TUMK-ELM: A Fast Unsupervised Heterogeneous Data Learning Approach

Advanced unsupervised learning techniques are an emerging challenge in the big data era due to the increasing requirements of extracting knowledge from a large amount of unlabeled heterogeneous data. Recently, many efforts of unsupervised learning have been done to effectively capture information fr...

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Bibliographic Details
Main Authors: Lingyun Xiang, Guohan Zhao, Qian Li, Wei Hao, Feng Li
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Unsupervised learning
heterogeneous data
clustering
extreme learning machine
multiple kernel learning
Online Access:https://ieeexplore.ieee.org/document/8384233/

Internet

https://ieeexplore.ieee.org/document/8384233/

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