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Sensors 2018, 18(1), 279; doi:10.3390/s18010279

Hierarchical Discriminant Analysis

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
These authors contributed equally to this work.
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Received: 6 December 2017 / Revised: 9 January 2018 / Accepted: 13 January 2018 / Published: 18 January 2018
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Abstract

The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA). It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms. View Full-Text
Keywords: Internet of Things; intelligent data; subspace learning; marginal fisher analysis; dimensionality reduction; discriminant neighborhood embedding Internet of Things; intelligent data; subspace learning; marginal fisher analysis; dimensionality reduction; discriminant neighborhood embedding
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Lu, D.; Ding, C.; Xu, J.; Wang, S. Hierarchical Discriminant Analysis. Sensors 2018, 18, 279.

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