Semi-Supervised and Unsupervised Extreme Learning Machines
By Gao Huang, Shiji Song, Jatinder N. D. Gupta, Cheng Wu. IEEE Transactions on Cybernetics, 2014
741 citations recorded by the source corpus.
Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/51886def908b16d11685ea23eb2124dfe961754f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Shiji Song
- G. Huang
- Gao Huang
- Keyou You
- Chi-Man Vong
- Liyanaarachchi Lekamalage Chamara Kasun
- Zuo Bai
- Quoc V. Le
In Computer Vision and Pattern Recognition
- Shiji Song
- Gao Huang
- G. Huang
- Keyou You
- Chi-Man Vong
- Liyanaarachchi Lekamalage Chamara Kasun
- Zuo Bai
- Luc Van Gool
Citations in the corpus
- What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle (2015, methodology): “Different combinations of kbk1 p and kHb Tk r2 q can be used and result in different learning algorithms for feature learning and clustering [7].”
- Local Receptive Fields Based Extreme Learning Machine (2015, background): “This section gives a brief review of ELM, CNN and HTM. Detailed reviews of ELM can be found in Huang et al. [13], [26].”
- Trends in extreme learning machines: A review (2015, methodology): “Huang (2014) analyzes the three learning principles of ELM.”
- Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems (2015, neutral): “Therefore, Huang et al [9] proposed a semi-supervised ELM for classification, in which a manifold regularization with graph Laplacian was set, and an unsupervised ELM was also explored for clustering.”