CNN_kernel_mine_b

Matrix: matrix should have samples as columns and rows as features

Numerical value: the number of nearest neighbours to use when calculating local sigma

Numerical value: the number of nearest neighbours to use when calculating common nearest neighbours

CNN_kernel_mine_b: fast adaptive density aware kernel

A versatile ultra-fast spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens local connections in dense regions in the graph. For integrating multi-view data and reducing noise we use a recently developed tensor product graph data integration and diffusion system. 'Spectrum' contains two techniques for finding the number of clusters (K); the classical eigengap method and a novel multimodality gap procedure. The multimodality gap analyses the distribution of the eigenvectors of the graph Laplacian to decide K and tune the kernel. 'Spectrum' is suited for clustering a wide range of complex data.

CNN_kernel_mine_b: CNN_kernel_mine_b: fast adaptive density aware kernel

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