Title :
A Nonlinear Semantic-Preserving Projection Approach to Visualize Multivariate Periodical Time Series
Author :
Blanchart, Pierre ; Depecker, Marine
Author_Institution :
LIST, CEA, Gif-sur-Yvette, France
Abstract :
A major drawback of nonlinear dimensionality reduction (DR) techniques is their inability to preserve some authentic information from the source domain, leading to projections that are often hard to interpret when it comes to observing anything other than the topological structure of the data. In this paper, we propose a nonlinear DR approach enforcing projection constraints resulting from an a priori knowledge about the structure of the data in multivariate periodical time series. We then propose several ways of exploiting this constrained projection to extract user-relevant information, such as the nominal behavior of a periodical dynamical system or the deviant behaviors which may occur at different time scales. The techniques are demonstrated on both a synthetic dataset composed of simulated multivariate data exhibiting a periodical behavior, and a real dataset corresponding to six months of sensor data acquisitions and recordings inside experimental buildings.
Keywords :
data reduction; data visualisation; time series; authentic information; multivariate periodical time series visualization; nonlinear DR approach; nonlinear dimensionality reduction techniques; nonlinear semantic-preserving projection approach; simulated multivariate data; source domain; synthetic dataset; user-relevant information; Data mining; Data models; Data visualization; Image color analysis; Monitoring; Time series analysis; Visualization; Data mining; deviant behaviors identification; high-dimensional; information visualization; monitoring; nonlinear dimensionality reduction (DR); pseudoperiodical time series; visual analytics; visual analytics.;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2013.2285928