Sentence examples for distance partition from inspiring English sources

Simulation results show that the proposed algorithm has a better performance, especially a better real-time performance, than the conventional distance partition and K-means++ methods.

Then, the spectral clustering method based on the neighbor propagation technique replaces the distance partition in the ET-GM-PHD algorithm.

So, the number of partitions grows rapidly as the target number increases, which implies that the distance partition consumes a large computational time, making the tracking algorithm intractable.

It is clear that the proposed method has the smallest partition number and least computational cost, followed by the distance partition method, and the K-means++ method has the biggest partition number and highest computational cost.

In order to demonstrate the performance of the proposed algorithm, we implement the proposed measurement partition method, the distance partition, and the K-means++ method under the framework of ET-GM-PHD filter [10].

It is clear that the proposed method has the smallest partition number and least computational cost although its accuracy is similar to that of the distance partition and obviously better than that of the K-means++ method.

Use the K-medoids algorithm [33] with the KL distance to partition the GMM set M into l clusters M i, i = 1,…, l, where each M i defines a subset of frames that are labeled by the GMMs in M i.   3.

Pool the distinct GMMs which are used to label the frames in X into a local GMM set M. Use the K-medoids algorithm [33] with the KL distance to partition the GMM set M into l clusters M i, i = 1,…, l, where each M i defines a subset of frames that are labeled by the GMMs in M i.

Two Pearson correlation analyses of pairwise distances were conducted at each shortening step: 1) correlation of the ML and uncorrected p distances for partition B and 2) correlation of the ML distances for partition A and B. The stopping point for site removal was determined as the point at which the two correlations showed a significant improvement (Goremykin et al. 2010).

Thus, we can use the distance between partitions to estimate the true pairwise distance.

A Well-Separated-Subgraph Decomposition method is employed to guarantee a distance-aware partition.