A novel online sequential extreme learning machine with L 2,1 -norm regularization for prediction problems
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A novel online sequential extreme learning machine with L2,1 -norm regularization for prediction problems Preeti1 · Rajni Bala1 · Ankita Dagar2 · Ram Pal Singh1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In today’s world, data is produced at a very high speed and used in a large number of prediction problems. Therefore, the sequential nature of learning algorithms is in demand for batch learning algorithms. This paper presents a novel online sequential algorithm for extreme learning machine with l2,1 -norm regularization (LR21OS-ELM) to handle the real-time sequential data. Wang et al. have given ELM with l2,1 -norm based regularization namely LR21-ELM. This method is a batch processing model which takes data in a single chunk. So, whenever a new chunk of data arrives the model has to be retrained which takes a lot of time and memory. The proposed sequential algorithm does not require building a new model each time data arrives. This will update the previous model with new data that will save time and memory. The l2,1 norm regularization is a structural sparse-inducing norm which is integrated with an online sequential learning algorithm to diminish the complexity of the learning model by eliminating the redundant neurons of OS-ELM model. This paper proposes an iterative bi-objective optimization algorithm to solve l2,1 norm-based minimization problem and to handle the real time sequential data. The proposed model can learn sequentially arriving data in the form of chunks where chunk size can be fixed or varying. The experimental study has been conducted on several benchmark datasets collected from different research domains to prove the generalization ability of the proposed algorithm. The obtained results show that LR21OSELM combines the advantages of l2,1 -norm regularization and online sequential learning of data and improves the prediction performance of the system. Keywords ELM · L2,1 -norm · LR21-ELM · LR21OS-ELM · Prediction · OS-ELM
1 Introduction In past decades, Artificial neural networks(ANNs) have widely been applied to a number of machine learning based
Preeti
preeti [email protected] Rajni Bala [email protected] Ankita Dagar [email protected] Ram Pal Singh [email protected] 1
Deen Dayal Upadhyaya College, University of Delhi, New Delhi, India
2
Indraprastha Institute of Information Technology Delhi, New Delhi, India
prediction problems. These networks are applied to various applications like pattern recognition [1], image-processing [2] and other research areas of importance. Among several possible network architectures, a feedforward neural network with single-hidden layer (SLFN) can find decision boundaries of various shapes for a given prediction problem if activation function is selected appropriately [3, 4]. Extreme learning machine(ELM) [5–9] is a batch learning technique which is based on single hidden layer neural network architecture. It is different from other neural networks as random numbers are used to assign initial parameters betwe
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