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Details of the Sorting Algorithms
If the calculated quantity d is greater than the adaptive template threshold value,
then a new template will be put into effect. The new template waveform t(i) is set
equal to the current average waveform s(i). The timestamp of the waveform being
processed when this occurs is recorded, as is the new t(i), so that Offline Sorter
Software can display a history of the template waveform changes made during
Adaptive Template Sorting.
Note that for small values of the adaptive template Weight W, the average
waveform changes only very slowly when the waveforms trend away from the
template, whereas for values of W near 1 the average waveform responds rapidly
to shifting waveforms. Also, a small value of the adaptive template threshold will
cause the algorithm to be eager to adopt new templates, whereas larger values
will make the algorithm more reluctant to change templates.
6.5.5 K-Means Algorithm
The K-Means algorithm is described in any pattern recognition textbook. The
algorithm runs in feature space, and requires starting positions for the clusters
(and thus implicitly the number of clusters) to be specified before the algorithm
begins.
Algorithm outline:
1
The user first sets initial cluster centers.
2
Each point is assigned to the cluster whose center is nearest, using a simple
2D or 3D Euclidean distance.
3
Cluster centers are then recalculated using the points assigned to each cluster,
using a simple center-of-gravity method.
Steps 2 and 3 are repeated until the cluster centers no longer change.
When the K-Means algorithm converges, an outlier removal algorithm runs.
The standard deviation of the distances from each point to its cluster center
computes. Any point that is more than a user-specified constant
(OutlierThreshold) times this standard deviation away from its cluster center is
removed from the cluster.
References:
Webb, Andrew, Statistical Pattern Recognition Second Edition, John Wiley and
Sons 2002
and many others...
6.5.6 Standard E-M Algorithm
The Expectation-Maximization algorithm is a widely-known iterative, parametric
approach that fits a normal mixture model (a mixture of several Gaussians) to the
available data points. It is so named because for each iteration the algorithm goes
through two phases, an Expectation calculation followed by a Maximization
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