Covariance matrices and probability distributions of point and vertically averaged hydraulic heads display similar key representative features and patterns.
This study proposes the causal feature selection (CFS) algorithm to select more representative features for better stock prediction modeling.
A key issue of quantitative investment (QI) product design is how to select representative features for stock prediction.
On the extracted brushstrokes, several representative features can then be calculated, like orientation, length, and width.
The local spectra statistical analysis is employed to extract representative features.
We have compared the temporal behavior of representative features from all categories.
The eyes are the most representative features of the human face, especially when masks are put on the face.
The most representative features, i.e. the ones with the smallest variance were Energy, SRE (GLRLM), and GLN (GLSZM).
Firstly, the representative features are extracted from the noisy vibration measurements to characterize the gearbox degradation conditions.
As an alternate to the AR parameters, in this paper, we propose to utilize reflection coefficients as representative features.
Rückstieß et al. [23] performed a SFS method to select the most representative features at each time step.
