idx= spectralcluster(X,k)partitions observations in then-by-pdata matrixXintokclusters using the spectral clustering algorithm (seeAlgorithms).spectralclusterreturns ann-by-1 vectoridxcontaining cluster indices of each observation. example idx= spectralcluster(S,k,'Distance','precomputed')returns a vec...
Anti-differentiating approximation algo- rithms: A case study with min-cuts, spectral, and flow. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 1018-1025, 2014.David Gleich and Michael Mahoney. Anti-differentiating approximation algorithms: A case study ...
One of the most commonly used unsupervised learning algorithms is clustering, which is widely used by data analysts and domain experts to group similar instances and explore hidden structures in a wide spectrum of fields, ranging from engineering, computer science and medical sciences to social scienc...
Vazirani, V.V.: Approximation Algorithms. Springer, Berlin (2013) Google Scholar Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11, 2837–2854 (2010) MathSci...
Graph cuts, which are a well-known class of spectral clustering algorithms, have been widely used in image segmentation [31], [13]. They are expensive in practice, as they require the computation of eigenvectors of smallest eigenvalues for very large Laplacian matrices. Fast graph-based algorithm...
Defining the appropriate kernel function allows one to use a range of different algorithms to analyze the data while, at the same time, avoiding many practical prediction problems. It is crucial for the performance of a system that the kernel function fits the learning target in some way, i....
The well-designed mask samples the space so that the spectra of each sampling point do not overlap each other on the detector, and can be used to acquire spectral video in real time without reconstruction algorithms, at the expense of spatial resolution. Instead of static binary encoded ...
13. 6.8.2 Cross-Dimensional Channel Attention In traditional physical optimization-based algorithms, hy- perparameters need to be defined manually and adjust to the optimal through a large number of experiments. Fur- thermore, in spectral super-resolution, differential treatment should ...
but with small distance. We then use the maximum value among all minimum distances between data points to get better clustering results. Using the estimated power parameter and the maximum value among minimum distances is able to improve spectral clustering. Some numerical data, real data sets, an...
A large proportion of existing spectral clustering algorithms use only one similarity measurement. However, there is a problem in that the clustering results based on different similarity matrices may be notably different11,27. Here, we introduce a multi-similarity method to the evolutionary spectral ...