The estimation of Intrinsic Dimension (ID) of data is particularly crucial in the unsupervised learning of nonlinear time series, as it essentially represents the minimum number of parameters to describe the data. The aim of this paper is to give both a new theoretical contribution and a machine learning algorithm that can be used for the ID estimation of time series. Several experimental results validate the proposed approach.
A machine learning method to determine intrinsic dimension of time series data / Turchetti, Claudio; Falaschetti, Laura. - (2017), pp. 303-307. (Intervento presentato al convegno 2017 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 tenutosi a Montreal, QC, Canada, Canada nel 14-16 November 2017) [10.1109/GlobalSIP.2017.8308653].
A machine learning method to determine intrinsic dimension of time series data
Turchetti, Claudio;Falaschetti, Laura
2017-01-01
Abstract
The estimation of Intrinsic Dimension (ID) of data is particularly crucial in the unsupervised learning of nonlinear time series, as it essentially represents the minimum number of parameters to describe the data. The aim of this paper is to give both a new theoretical contribution and a machine learning algorithm that can be used for the ID estimation of time series. Several experimental results validate the proposed approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
