Variational Information Bottleneck Model for Accurate Indoor Position Recognition
| Autor: | Franck Gechter, Weizhu Qian |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 0209 industrial biotechnology Artificial neural network Computer science business.industry Deep learning Fingerprint (computing) Information bottleneck method 02 engineering and technology Overfitting Fingerprint recognition computer.software_genre Machine Learning (cs.LG) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer Encoder Dropout (neural networks) |
| Zdroj: | ICPR |
| DOI: | 10.1109/icpr48806.2021.9412651 |
| Popis: | Recognizing user location with WiFi fingerprints is a popular approach for accurate indoor positioning problems. In this work, our goal is to interpret WiFi fingerprints into actual user locations. However, WiFi fingerprint data can be very high dimensional in some cases, we need to find a good representation of the input data for the learning task first. Otherwise, using neural networks will suffer from severe overfitting. In this work, we solve this issue by combining the Information Bottleneck method and Variational Inference. Based on these two approaches, we propose a Variational Information Bottleneck model for accurate indoor positioning. The proposed model consists of an encoder structure and a predictor structure. The encoder is to find a good representation in the input data for the learning task. The predictor is to use the latent representation to predict the final output. To enhance the generalization of our model, we also adopt the Dropout technique for each hidden layer of the decoder. We conduct the validation experiments on a real-world dataset. We also compare the proposed model to other existing methods so as to quantify the performances of our method. |
| Databáze: | OpenAIRE |
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