Prototype Regularized Manifold Regularization Technique for Semi-Supervised Online Extreme Learning Machine

Muhammad Zaly Shah, Muhammad Zafran and Zainal, Anazida and Ghaleb, Fuad A. and Al-Qarafi, Abdulrahman and Saeed, Faisal (2022) Prototype Regularized Manifold Regularization Technique for Semi-Supervised Online Extreme Learning Machine. Sensors, 22 (9). p. 3113. ISSN 1424-8220

[img]
Preview
Text
sensors-22-03113-v2.pdf - Published Version
Available under License Creative Commons Attribution.

Download (7MB)

Abstract

Data streaming applications such as the Internet of Things (IoT) require processing or predicting from sequential data from various sensors. However, most of the data are unlabeled, making applying fully supervised learning algorithms impossible. The online manifold regularization approach allows sequential learning from partially labeled data, which is useful for sequential learning in environments with scarcely labeled data. Unfortunately, the manifold regularization technique does not work out of the box as it requires determining the radial basis function (RBF) kernel width parameter. The RBF kernel width parameter directly impacts the performance as it is used to inform the model to which class each piece of data most likely belongs. The width parameter is often determined off-line via hyperparameter search, where a vast amount of labeled data is required. Therefore, it limits its utility in applications where it is difficult to collect a great deal of labeled data, such as data stream mining. To address this issue, we proposed eliminating the RBF kernel from the manifold regularization technique altogether by combining the manifold regularization technique with a prototype learning method, which uses a finite set of prototypes to approximate the entire data set. Compared to other manifold regularization approaches, this approach instead queries the prototype-based learner to find the most similar samples for each sample instead of relying on the RBF kernel. Thus, it no longer necessitates the RBF kernel, which improves its practicality. The proposed approach can learn faster and achieve a higher classification performance than other manifold regularization techniques based on experiments on benchmark data sets. Results showed that the proposed approach can perform well even without using the RBF kernel, which improves the practicality of manifold regularization techniques for semi-supervised learning.

Item Type: Article
Identification Number: https://doi.org/10.3390/s22093113
Dates:
DateEvent
11 April 2022Accepted
19 April 2022Published Online
Uncontrolled Keywords: machine learning; semi-supervised learning; manifold regularization; sequential learning; Internet of Things
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Faisal Saeed
Date Deposited: 03 May 2022 13:49
Last Modified: 03 May 2022 13:49
URI: http://www.open-access.bcu.ac.uk/id/eprint/13200

Actions (login required)

View Item View Item

Research

In this section...