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Found 5 papers, 1 papers with code
Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes
We consider the problem of estimating the latent state of a spatiotemporally evolving continuous function using very few sensor measurements.
Reduced-Set Kernel Principal Components Analysis for Improving the Training and Execution Speed of Kernel Machines
This paper presents a practical, and theoretically well-founded, approach to improve the speed of kernel manifold learning algorithms relying on spectral decomposition.
Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm
Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results.
Deterministic Initialization of the K-Means Algorithm Using Hierarchical Clustering
Experiments on a large and diverse collection of data sets from the UCI Machine Learning Repository demonstrate that Var-Part and PCA-Part are highly competitive with one of the best random initialization methods to date, i. e., k-means++, and that the proposed approach significantly improves the performance of both hierarchical methods.
A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm
K-means is undoubtedly the most widely used partitional clustering algorithm.
