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Therefore, we try to find the better compromise between data samples and smoothness.
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The restoration process was performed between two data samples and hence the first and last data samples have also been considered as regular data points.
Signal restoration was performed on each individual part by finding the best compromise between the data samples and the smoothing criteria.
where R ds ′ ∈ R N s × N s and R t ′ ∈ R N t × N t are the cross-correlation matrices between the data samples and the interpolations in the space domain and time domain, respectively.
In order to find the best compromise between the data samples and smoothing criteria, two optimization factors for data samples and smoothness (w M and w S, respectively) has been introduced such that Eq. (5) will become: Y i k + 1 = Y i k + w M λ i k - ∂ C i M S E, k ∂ Y i + w S γ i k - ∂ C i M S O, k ∂ Y i (14).
Then, for the second step, the distances between each data sample and the centroids are calculated.
The first distance-based feature means the distance between the data sample and its cluster center.
The second one is the sum of the distances between the data sample and other cluster centers.
In this article, we will utilize the distance between a data sample and its intra-cluster center as a new feature, called Feature 1.
After the cluster center for each class is identified, the distance between a data sample and its cluster center (or intra-cluster center) can be calculated.
Here, we define another new feature, called Feature 2, as the sum of the distances between a data sample and its extra-cluster centers.
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CEO of Professional Science Editing for Scientists @ prosciediting.com