SpectralClustering.jl:用Julia编写的光谱聚类算法

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SpectralClustering.jl:用Julia编写的光谱聚类算法
SpectralClustering_jl-master.zip
内容介绍
# SpectralClustering - [Documentation](https://lucianolorenti.github.io/SpectralClustering.jl/latest) - Check out the [Examples](https://lucianolorenti.github.io/SpectralClustering.jl/latest/notebooks/Index.html) [![Coverage Status](https://coveralls.io/repos/github/lucianolorenti/SpectralClustering.jl/badge.svg?branch=master)](https://coveralls.io/github/lucianolorenti/SpectralClustering.jl?branch=master) The library provides functions that allow: * Build the affinity matrix. * Perform the embedding of the patterns in the space spanned by the eigenvectors of the matrices derived from the affinity matrix. * Obtain an approximation of the eigenvectors in order to reduce the computational complexity. * Exploiting information from multiple views. Corresponding nodes in each graph should have the same cluster membership. * Clusterize the eigenvector space. # Methods implemented * Graph construction * [Self-Tuning Spectral Clustering](https://papers.nips.cc/paper/2619-self-tuning-spectral-clustering.pdf) * Embedding * [Normalized cuts and image segmentation](https://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf) * [On Spectral Clustering: Analysis and an algorithm](https://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf) * [Understanding Popout through Repulsion](https://pdfs.semanticscholar.org/019c/099ab01902416a625a9d18a36e61b88f5a3d.pdf) * [Segmentation Given Partial Grouping Constraints](http://www.cs.cmu.edu/~xingyu/papers/yu_bias.pdf) * Approximate embedding * [Spectral grouping using the nystrom method](https://people.eecs.berkeley.edu/~malik/papers/FBCM-nystrom.pdf) * [Nystrom sampling depends on the eigenspectrum shape of the data](https://openreview.net/pdf?id=HJZvjvJPf) * [Large Scale Spectral Clustering with Landmark-Based Representation](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.365.6933&rep=rep1&type=pdf) * Multiple views * Kernel Addition * Kernel Product * Feature Concatenation (in the examples section) * [Co-regularized Multi-view Spectral Clustering](https://papers.nips.cc/paper/4360-co-regularized-multi-view-spectral-clustering.pdf) * Incremental * TODO [Incremental spectral clustering by efficiently updating the eigen-system](https://www.sciencedirect.com/science/article/pii/S0031320309002209/pdfft?md5=dc50ecba5ab9ab23ea239ef89244800a&pid=1-s2.0-S0031320309002209-main.pdf) * Clusterize * [Multiclass Spectral Clustering](http://www.public.asu.edu/~jye02/CLASSES/Spring-2007/Papers/PAPERS/295_yu_s.pdf) * KMeans via [Clustering.jl](https://github.com/JuliaStats/Clustering.jl) The documentation and the library is still a work in progress.
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