sigma_finder

Data frame or matrix: with points as columns, features as rows

Numerical value: the number of nearest neighbours to use (default=3)

This is a heuristic to find the sigma for the kernel from the Ng spectral clustering algorithm. 
It returns a global sigma. It uses the mean K nearest neighbour distances of all samples to
determine sigma.

A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.

Christopher John

sigma_finder: sigma_finder: heuristic to find sigma for the Ng kernel

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