TY - GEN
T1 - Linguistically described covariance matrix estimation
AU - Przybyła, Tomasz
AU - Pander, Tomasz
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG.
PY - 2018
Y1 - 2018
N2 - In this paper we present a covariance matrix estimation method based on linguistically described data samples. The linguistic variable describes a real data samples that could be used for a calculation of the covariance matrix. In most cases, real dataset contains noise samples that manifest as outliers. Hence, the covariance matrix estimation problem can be formulated in the following way: take only these data samples that are not outliers. In this way, the influence of outliers is confined and thereby increases the robustness of the estimation. Linguistic variable distance takes values that are fuzzy sets. In the simplest case, the distance can be small or large. The distance is calculated between the data samples and the dataset center. In this paper we used generalized sample mean estimator for the calculation of the dataset center. The proposed method was used in the nonlinear state-space projection method (NSSP) where the estimation of a covariance matrix plays crucial role. The modified NSSP method was applied to ECG signal processing.
AB - In this paper we present a covariance matrix estimation method based on linguistically described data samples. The linguistic variable describes a real data samples that could be used for a calculation of the covariance matrix. In most cases, real dataset contains noise samples that manifest as outliers. Hence, the covariance matrix estimation problem can be formulated in the following way: take only these data samples that are not outliers. In this way, the influence of outliers is confined and thereby increases the robustness of the estimation. Linguistic variable distance takes values that are fuzzy sets. In the simplest case, the distance can be small or large. The distance is calculated between the data samples and the dataset center. In this paper we used generalized sample mean estimator for the calculation of the dataset center. The proposed method was used in the nonlinear state-space projection method (NSSP) where the estimation of a covariance matrix plays crucial role. The modified NSSP method was applied to ECG signal processing.
KW - Fuzzy clustering
KW - Projective filtering
KW - Robust covariance matrix
UR - https://www.scopus.com/pages/publications/85030788273
U2 - 10.1007/978-3-319-67792-7_24
DO - 10.1007/978-3-319-67792-7_24
M3 - Conference contribution
AN - SCOPUS:85030788273
SN - 9783319677910
T3 - Advances in Intelligent Systems and Computing
SP - 238
EP - 248
BT - Man-Machine Interactions 5 - 5th International Conference on Man-Machine Interactions, ICMMI 2017
A2 - Gruca, Aleksandra
A2 - Czachorski, Tadeusz
A2 - Harezlak, Katarzyna
A2 - Kozielski, Stanislaw
A2 - Piotrowska, Agnieszka
A2 - Czachorski, Tadeusz
PB - Springer Verlag
T2 - 5th International Conference on Man-Machine Interactions, ICMMI 2017
Y2 - 3 October 2017 through 6 October 2017
ER -