TY  - EDBOOK
AB  - 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.
DA  - 2015
KW  - dimensionality reduction
KW  - low-dimensional representations
KW  - sensor data
KW  - smart home
LA  - eng
PY  - 2015
TI  - Towards Dimensionality Reduction for Smart Home Sensor Data
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-27833696
Y2  - 2024-11-22T03:18:22
ER  -