Parallel Implementation of Neighbourhood Repulsed Correlation Metric Learning
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2019 by IJETT Journal|
|Year of Publication : 2019|
|Authors : Saurav Rai, Pallav Kumar Baruah
|DOI : 10.14445/22315381/IJETT-V67I4P209|
MLA Style: Saurav Rai, Pallav Kumar Baruah "Parallel Implementation of Neighbourhood Repulsed Correlation Metric Learning" International Journal of Engineering Trends and Technology 67.4 (2019): 38-41.
APA Style:Saurav Rai, Pallav Kumar Baruah (2019). Parallel Implementation of Neighbourhood Repulsed Correlation Metric Learning. International Journal of Engineering Trends and Technology, 67(4), 38-41.
Metric learning has attracted a lot of attention in recent times due to its usefulness and benefits in the domains of Machine learning, Natural Language Processing, and Big data. It is used as a pre-processing step in many Machine learning algorithms. However, as the size of the input data increases, the amount of time used for learning the metric which captures the semantic nature of the data also increases. In this paper, we have implemented parallel (CUDA) version of one such metric learning algorithm called as Neighbourhood Repulsed Correlation Metric Learning (NRCML). To illustrate the applicability of this parallel implementation, we derive motivation from the experimental evaluation that shows our implementation has greatly reduced the training time of the original sequential implementation. The proposed method has shown improvement in the performance of about 100x and above
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Metric Learning, CUDA, NRCML.