Towards Dimensionality Reduction for Smart Home Sensor Data

Mokbel B, Schulz A (2015)
In: Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015). Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports(3). 41-48.

Konferenzbeitrag | Veröffentlicht| Englisch
 
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Hammer, Barbara; Martinetz, Thomas; Villmann, Thomas
Abstract / Bemerkung
In this paper, we investigate in how far nonlinear dimensionality reduction (DR) techniques can be utilized to tackle particular challenges of sensor data from smart home environments. Smart homes often contain a large number of sensors of various types, providing output in real time, which results in a sequence of high-dimensional, heterogeneous data vectors. We propose that DR techniques can provide a truthful low-dimensional representation (i.e. a compression) of this kind of data, together with a corresponding reconstruction (i.e. decompression). This yields an automatic fusion of uncoordinated raw sensor signals, as well as an economical storage format, with a certain robustness against sensor failure. In proof-of-concept experiments, we present first empirical results to test our approach based on real-world data.
Stichworte
dimensionality reduction; low-dimensional representations; sensor data; smart home
Erscheinungsjahr
2015
Titel des Konferenzbandes
Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015)
Ausgabe
3
Seite(n)
41-48
Konferenz
New Challenges in Neural Computation
Konferenzort
Aachen
Konferenzdatum
2015-10-10
ISSN
1865-3960
Page URI
https://pub.uni-bielefeld.de/record/2783369

Zitieren

Mokbel B, Schulz A. Towards Dimensionality Reduction for Smart Home Sensor Data. In: Hammer B, Martinetz T, Villmann T, eds. Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015). Machine Learning Reports. 2015: 41-48.
Mokbel, B., & Schulz, A. (2015). Towards Dimensionality Reduction for Smart Home Sensor Data. In B. Hammer, T. Martinetz, & T. Villmann (Eds.), Machine Learning Reports. Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015) (pp. 41-48).
Mokbel, B., and Schulz, A. (2015). “Towards Dimensionality Reduction for Smart Home Sensor Data” in Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015), Hammer, B., Martinetz, T., and Villmann, T. eds. Machine Learning Reports 41-48.
Mokbel, B., & Schulz, A., 2015. Towards Dimensionality Reduction for Smart Home Sensor Data. In B. Hammer, T. Martinetz, & T. Villmann, eds. Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015). Machine Learning Reports. pp. 41-48.
B. Mokbel and A. Schulz, “Towards Dimensionality Reduction for Smart Home Sensor Data”, Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015), B. Hammer, T. Martinetz, and T. Villmann, eds., Machine Learning Reports, 2015, pp.41-48.
Mokbel, B., Schulz, A.: Towards Dimensionality Reduction for Smart Home Sensor Data. In: Hammer, B., Martinetz, T., and Villmann, T. (eds.) Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015). Machine Learning Reports. p. 41-48. (2015).
Mokbel, Bassam, and Schulz, Alexander. “Towards Dimensionality Reduction for Smart Home Sensor Data”. Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015). Ed. Barbara Hammer, Thomas Martinetz, and Thomas Villmann. 2015. Machine Learning Reports. 41-48.
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