19,961 research outputs found
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
Heterogeneous information networks (HINs) are ubiquitous in real-world
applications. In the meantime, network embedding has emerged as a convenient
tool to mine and learn from networked data. As a result, it is of interest to
develop HIN embedding methods. However, the heterogeneity in HINs introduces
not only rich information but also potentially incompatible semantics, which
poses special challenges to embedding learning in HINs. With the intention to
preserve the rich yet potentially incompatible information in HIN embedding, we
propose to study the problem of comprehensive transcription of heterogeneous
information networks. The comprehensive transcription of HINs also provides an
easy-to-use approach to unleash the power of HINs, since it requires no
additional supervision, expertise, or feature engineering. To cope with the
challenges in the comprehensive transcription of HINs, we propose the HEER
algorithm, which embeds HINs via edge representations that are further coupled
with properly-learned heterogeneous metrics. To corroborate the efficacy of
HEER, we conducted experiments on two large-scale real-words datasets with an
edge reconstruction task and multiple case studies. Experiment results
demonstrate the effectiveness of the proposed HEER model and the utility of
edge representations and heterogeneous metrics. The code and data are available
at https://github.com/GentleZhu/HEER.Comment: 10 pages. In Proceedings of the 24th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, London, United Kingdom,
ACM, 201
Latent Embeddings for Collective Activity Recognition
Rather than simply recognizing the action of a person individually,
collective activity recognition aims to find out what a group of people is
acting in a collective scene. Previ- ous state-of-the-art methods using
hand-crafted potentials in conventional graphical model which can only define a
limited range of relations. Thus, the complex structural de- pendencies among
individuals involved in a collective sce- nario cannot be fully modeled. In
this paper, we overcome these limitations by embedding latent variables into
feature space and learning the feature mapping functions in a deep learning
framework. The embeddings of latent variables build a global relation
containing person-group interac- tions and richer contextual information by
jointly modeling broader range of individuals. Besides, we assemble atten- tion
mechanism during embedding for achieving more com- pact representations. We
evaluate our method on three col- lective activity datasets, where we
contribute a much larger dataset in this work. The proposed model has achieved
clearly better performance as compared to the state-of-the- art methods in our
experiments.Comment: 6pages, accepted by IEEE-AVSS201
Human Activity Recognition Using Deep Learning Networks with Enhanced Channel State Information
© 2018 IEEE. Channel State Information (CSI) is widely used for device free human activity recognition. Feature extraction remains as one of the most challenging tasks in a dynamic and complex environment. In this paper, we propose a human activity recognition scheme using Deep Learning Networks with enhanced Channel State information (DLN-eCSI). We develop a CSI feature enhancement scheme (CFES), including two modules of background reduction and correlation feature enhancement, for preprocessing the data input to the DLN. After cleaning and compressing the signals using CFES, we apply the recurrent neural networking (RNN) to automatically extract deeper features and then the softmax regression algorithm for activity classification. Extensive experiments are conducted to validate the effectiveness of the proposed scheme
- …