Rank Based Pseudoinverse Computation in Extreme Learning Machine for large Datasets
Ramesh Ragala1, G. Bharadwaja Kumar2
1Ramesh Ragala, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, India.
2G. Bharadwaja Kumar, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, India.
Manuscript received on 03 July 2019 | Revised Manuscript received on 07 July 2019 | Manuscript published on 30 August 2019 | PP: 1341-1346 | Volume-8 Issue-10, August 2019 | Retrieval Number: I8439078919/2019©BEIESP | DOI: 10.35940/ijitee.I8439.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Extreme Learning Machine (ELM) is an efficient and effective least-square-based learning algorithm for classification, regression problems based on single hidden layer feed-forward neural network (SLFN). It has been shown in the literature that it has faster convergence and good generalization ability for moderate datasets. But, there is great deal of challenge involved in computing the pseudoinverse when there are large numbers of hidden nodes or for large number of instances to train complex pattern recognition problems. To address this problem, a few approaches such as EM-ELM, DF-ELM have been proposed in the literature. In this paper, a new rank-based matrix decomposition of the hidden layer matrix is introduced to have the optimal training time and reduce the computational complexity for a large number of hidden nodes in the hidden layer. The results show that it has constant training time which is closer towards the minimal training time and very far from worst-case training time of the DF-ELM algorithm that has been shown efficient in the recent literature.
Keywords: Extreme Learning Machine, Moore-Penrose Matrix, Machine Learning, Classification, Matrix Decomposition
Scope of the Article: Artificial Intelligence and Machine Learning