GitHub - LiHeUA/FastESC: Fast Large-Scale Spectral Clustering via Explicit Feature Mapping

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Fast Large-Scale Spectral Clustering via Explicit Feature Mapping

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Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
EBMM_Release		EBMM_Release
.gitattributes		.gitattributes
.gitignore		.gitignore
ExplicitFeatureMapping_RFF.m		ExplicitFeatureMapping_RFF.m
FastESC.m		FastESC.m
FastESC_LargeScale.m		FastESC_LargeScale.m
IG.m		IG.m
Iris.mat		Iris.mat
README.txt		README.txt
demoFastESC.m		demoFastESC.m
demoFastESC_EMNIST.m		demoFastESC_EMNIST.m
myNCut.m		myNCut.m
pdist2_my.m		pdist2_my.m

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This set of m-files demonstrates the FastESC clustering algorithm and the Extended Basic Matrix Multiplication algorithm in [1].  

"demoFastESC.m" shows FastESC on Iris dataset.

"demoFastESC_EMNIST.m" shows FastESC on EMNIST-Digits dataset.

"demo_BMM.m" shows the Basic Matrix Multiplication algorithm in
P. Drineas, R. Kannan, and M. W. Mahoney, "Fast monte carlo algorithms for matrices i: Approximating matrix multiplication," SIAM Journal on Computing, vol. 36, no. 1, pp. 132-157, 2006.

"demo_EBMM.m" shows the Extended Basic Matrix Multiplication algorithm, or Algorithm 1 and Theorem 1 in [1].

Run those demos in Matlab.

Acknowledgment:

[1] Li He, Nilanjan Ray, Yisheng Guan and Hong Zhang. Fast Large-Scale Spectral Clustering via Explicit Feature Mapping. To appear in IEEE Transactions on Cybernetics.

Please cite this paper if you use the code to generate your results.

heli@gdut.edu.cn