An Efficient Approach for Iterative Learning Algorithms
G. Abinaya1, Anirudh Sundararaman2, R. Ashwin3, Ravi Teja4, Vinay Motghare5
1G. Abinaya, Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, (Tamil Nadu), India.
2Anirudh Sundararaman, B.Tech Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, (Tamil Nadu), India.
3R. Ashwin, B.Tech Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, (Tamil Nadu), India.
4Ravi Teja, B.Tech Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, (Tamil Nadu), India.
5Vinay Motghare, B.Tech Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, (Tamil Nadu), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 1847-1851 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2640037619/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: In this paper, a framework which takes into account machine learning for the analysis of massive datasets is proposed. The framework maps the algorithms to the respective platform so as to extract maximum resource efficiency. In addition, the framework takes into account a data projection technique called as Elastic Dictionary to form sparse representation of the underlying data. By this way, the resource efficiency is optimized leading to reduction in the cost associated with the performance. The framework represents a model and shows the performance metrics in accordance with their respective runtime and storage. An additional application program interface takes into account the applicability of the framework to the underlying platform or datasets. The framework is based on the union of both the content and platform aware methodologies so as to make the machine learning algorithms to utilize the resources efficiently.
Keywords: Cholesky Factorization, Elastic Dictionary, Orthogonal matching pursuit algorithm, Sparse Approximation
Scope of the Article: Deep Learning