2017/11/22 by Yonathan Murin, Murin, Yonathan · 1 citation
Computer Science · #Blind Source Separation Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Neural Networks and Applications #Target Tracking and Data Fusion in Sensor Networks
paper · pdf · doi:10.48550/arxiv.1711.08516
openalex publication_date 2017/11/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This report studies data-driven estimation of the directed information (DI) measure between twoem discrete-time and continuous-amplitude random process, based on the k-nearest-neighbors (k-NN) estimation framework. Detailed derivations of two k-NN estimators are provided. The two estimators differ in the metric based on which the nearest-neighbors are found. To facilitate the estimation of the DI measure, it is assumed that the observed sequences are (jointly) Markovian of order m. As m is generally not known, a data-driven method (that is also based on the k-NN principle) for estimating m from the observed sequences is presented. An exhaustive numerical study shows that the discussed k-NN estimators perform well even for relatively small number of samples (few thousands). Moreover, it is shown that the discussed estimators are capable of accurately detecting linear as well as non-linear causal interactions.