Fog Computing, Deep Learning and Big Data Analytics-Research Directions by C. S. R. Prabhu

Author:C. S. R. Prabhu
Language: eng
Format: epub
ISBN: 9789811332098
Publisher: Springer Singapore

One challenge today is to deal with fast-moving incremental input data streams. How to adopt Deep Learning to such requirement? There is a requirement of Deep Learning algorithms which can learned from the continuous input of stream data.

Zhou et al. [229] deployed denoising autoencoder [230] for incremental feature learning. Denoising autoencoders are a variation of regular autoencoders which are capable of extracting features from corrupted inputs such that the extracted features are robust and unaffected by noise but can be deployed for classification purposes. This is achieved by deploying a hidden layer for denoising.

Calandra et al. [231] demonstrate Adaptive Deep Belief Network to learn from online, nonstationary stream data.(ii)High-Dimensional Data

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