All the data samples were digitized with sampling frequency 12.5 kHz.
The data samples were acquired at a sampling period of 2 ms over a 5 s window period.
The data samples are collected from a real experiment and are noisy in nature.
The data samples were collected by the CMS experiment at the CERN LHC, at a nucleon-nucleon center-of-mass energy of 5.02 TeV.
The data samples used in the analysis correspond to integrated luminosities of 28 pb−1 and 35 nb−1 for pp and pPb collisions, respectively.
The data samples correspond to integrated luminosities of 5.1 fb -1) at root s = 7 TeV and 19.7 fb -1) at 8 TeV.
The data samples correspond to integrated luminosities of 71 and 44 pb -1) for | y| < 3 and 3.2 < | y| < 4.7, respb -1vely.
However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples.
We outline a K-way spectral clustering algorithm able to integrate pairwise relationships between the data samples.
Linear and unstable regimes were also identified in the data samples, as seen in previous studies by the authors.
The GMM is then applied to model and predict the drug release profiles based on the data samples collected from the experiments.
