Affiliations: School of Computer Science and Engineering, Vellore Institute of Technology, VIT Chennai, Tamilnadu, India
Correspondence:
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Corresponding author. Ramesh Ragala, School of Computer Science and Engineering, Vellore Institute of Technology, VIT Chennai, Tamilnadu, India. E-mail: [email protected].
Abstract: Due to the massive memory and computational resources required to build complex machine learning models on large datasets, many researchers are employing distributed environments for training the models on large datasets. The parallel implementations of Extreme Learning Machine (ELM) with many variants have been developed using MapReduce and Spark frameworks in the recent years. However, these approaches have severe limitations in terms of Input-Output (I/O) cost, memory, etc. From the literature, it is known that the complexity of ELM is directly propositional to the computation of Moore-Penrose pseudo inverse of hidden layer matrix in ELM. Most of the ELM variants developed on Spark framework have employed Singular Value Decomposition (SVD) to compute the Moore-Penrose pseudo inverse. But, SVD has severe memory limitations when experimenting with large datasets. In this paper, a method that uses Recursive Block LU Decomposition to compute the Moore-Penrose generalized inverse over the Spark cluster has been proposed to reduce the computational complexity. This method enhances the ELM algorithm to be efficient in handling the scalability and also having faster execution of the model. The experimental results have shown that the proposed method is efficient than the existing algorithms available in the literature.
Keywords: Recursive Block LU Decomposition, Extreme Learning Machine, Apache Spark, Moore-Penrose pseudoinverse, Big Data
