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Exploration of Human Activities Using Sensing Data via Deep Embedded Determination

  • Yiqi Wang
  • En Zhu
  • Qiang Liu
  • Yingwen Chen
  • Jianping Yin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10874)

Abstract

Clustering analysis is one of promising techniques of uncovering different types of human activities from a set of ubiquitous sensing data in an unsupervised manner. Previous work proposes deep clustering to learn feature representations that favor clustering tasks. However, these algorithms assume that the number of clusters is known a priori, which is often impractical in the real world. Determining the number of clusters from high dimensional data is challenging. On the other hand, the lack of the number of clusters make it difficult to extract low dimensional features appropriate for clustering. In this paper, we propose Deep Embedding Determination (DED), a method that can determine the number of clusters and extract appropriate features for the high dimensional real data. Our experimental evaluation on different datasets shows the effectiveness of DED, and the excellent performance of DED in exploring the human activities using sensing data.

Keywords

Human activity analysis Deep clustering Determination of cluster number Sensing data 

Notes

Acknowledgments

This work was supported by the National Key R&D Program of China 2018YFB1003202 and the National Natural Science Foundation of China (Project no. 61773392, 61702539 and 61672528).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.Dongguan University of TechnologyDongguanChina

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