In this paper, we propose an extended version to be applied in the context of non-stationary stream of partially labeled data.
Our sparse recovery algorithm is based on ℓ1-homotopy with a learned dictionary, which compared to alternative techniques/dictionaries is more accurate, fast, and easily-implemented for dynamic, non-stationary, streaming AE signals.
This paper has introduced a semi-supervised graph-based algorithm suitable for non-stationary streaming application, particularly when only a small portion of the acquired data presents label.
To tackle the issues of anomalies and non-stationarity, we employ an extension of the possibilistic clustering approach [34, 35] named Graded Possibilistic c-Means [15, 38] as a means to perform clustering of the non-stationary streaming data and employ the knowledge accumulated into the clusters to build a robust, accurate short-term traffic forecaster.
Wind tunnel experiments have been conducted on a cylindrical vortex embedded in a low turbulence stationary horizontal stream, running through a two-dimensional narrow vertical woven fence located on the wind tunnel floor.
The nonlinear propagation of the dust electron-acoustic waves in a dust electron-acousticg of cold and hot electrons, stationary and streaming ions, and charge fluctuating stationary dust has been investigated by employing the reductive perturbation method.
Cylindrical and spherical dust-electron-acoustic (DEA) shock waves propagating in a dusty plasma (containing cold inertial electrons, hot Maxwellian electrons, stationary and streaming ions, and charge fluctuating stationary dust) are theoretically investigated by reductive perturbation method.
Here, we briefly present another visualization technique called component planes [3], that further motivates the application of UbiSOM to a non-stationary data stream.
