Journal ArticleDOI
Deep Computation Model for Unsupervised Feature Learning on Big Data
TLDR
A deep computation model for feature learning on big data, which uses a tensor to model the complex correlations of heterogeneous data and is efficient to perform feature learning when evaluated using the STL-10, CUAVE, SANE and INEX datasets.Abstract:
Deep learning has been successfully applied to feature learning in speech recognition, image classification and language processing. However, current deep learning models work in the vector space, resulting in the failure to learn features for big data since a vector cannot model the highly non-linear distribution of big data, especially heterogeneous data. This paper proposes a deep computation model for feature learning on big data, which uses a tensor to model the complex correlations of heterogeneous data. To fully learn the underlying data distribution, the proposed model uses the tensor distance as the average sum-of-squares error term of the reconstruction error in the output layer. To train the parameters of the proposed model, the paper designs a high-order back-propagation algorithm (HBP) by extending the conventional back-propagation algorithm from the vector space to the high-order tensor space. To evaluate the performance of the proposed model, we carried out the experiments on four representative datasets by comparison with stacking auto-encoders and multimodal deep learning models. Experimental results clearly demonstrate that the proposed model is efficient to perform feature learning when evaluated using the STL-10, CUAVE, SANE and INEX datasets.read more
Citations
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A survey on deep learning for big data
TL;DR: The emerging researches of deep learning models for big data feature learning are reviewed and the remaining challenges of big data deep learning are pointed out and the future topics are discussed.
Journal ArticleDOI
A Survey on Deep Learning for Multimodal Data Fusion
TL;DR: This review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals of multi-modality deep learning fusion method and to motivate new multimodAL data fusion techniques of deep learning.
Journal ArticleDOI
A Cloud-Edge Computing Framework for Cyber-Physical-Social Services
TL;DR: A tensor-based cloud-edge computing framework that mainly includes the cloud and edge planes is presented that is used to process large-scale, long-term, global data, which can be used to obtain decision making information such as the feature, law, or rule sets.
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Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things
TL;DR: Experimental results show that the deep convolutional computation model can give higher classification accuracy than the deep computation model or the multimodal model for big data in IoT.
Journal ArticleDOI
A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion
TL;DR: The properties of IoTData, a number of IoT data fusion requirements including the ones about security and privacy, classify the IoT applications into several domains and a thorough review on the state-of-the-art of data fusion in main IoT application domains are investigated.
References
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