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Short review of dimensionality reduction methods for failure detection

Pocha A., Misztal K., Morkisz P.

Schedae Informaticae

2017

Vol. 26

69--78

Size of a dataset is often a challenge in real-life applications. Especially, when working with time series data, when the next sample is produced every few milliseconds and can include measurements from hundreds of sensors, one has to take dimensionality of the data into consideration. In this work, we compare various dimensionality reduction methods for time series data and check their performance on a failure detection task. We work on sensory data coming from existing machines.