isomap降维matlab代码-cclap:基于互协方差的亲和力以利用未开发的图拉普拉斯连通性

# CCLap Cross-covariance based affinity to exploit the unexplored graph Laplacian connectivity ## Requirements 1. Matlab > R2016a<br/> 1. Compatible C++ Mex compiler ## How to use? 1. Run **setup.m** to perform initial setup.<br/> 1. Run **divRun_v2.m** to obtain classifier's performance.<br/> 1. Run **nldr_interactive.m** to open the interactive window.<br/> <figure> <figcaption>Figure: nldr_interactive.m</figcaption> </figure> ## Classifier results <figure> <figcaption>Table: Mean error(± Standard deviation) LapSVM (Test)</figcaption> </figure><br/> <figure> <figcaption>Table: Mean error(± Standard deviation) LapSVM (Unlabeled)</figcaption> </figure><br/> <figure> <figcaption>Table: Mean error(± Standard deviation) LapRLSC (Test)</figcaption> </figure><br/> <figure> <figcaption>Table: Mean error(± Standard deviation) LapRLSC (Unlabeled)</figcaption> </figure> ## References <ol> <li>Manifold Regularization: a Geometric Framework for Learning from Examples, M. Belkin, P. Niyogi, V. Sindhwani, University of Chicago Computer Science Technical Report TR-2004-06, 2004, http://manifold.cs.uchicago.edu/manifold_regularization/manifold.html</li> <li>Nonlinear dimensionality reduction by locally linear embedding, Sam Roweis & Lawrence Saul, Science, v.290 no.5500 , Dec.22, 2000. pp.2323--2326, https://cs.nyu.edu/~roweis/lle/code.html</li> <li>A global geometric framework for nonlinear dimensionality reduction, Tenenbaum, J., de Silva, V., Langford, J., Science 290, 2319-2323, 2000, http://web.mit.edu/cocosci/isomap/code/Isomap.m</li> <li>Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment, Zhang, Zhenyue, Hongyuan Zha, SIAM Journal on Scientific Computing, 26 (1), 313–338, 2004, https://arxiv.org/abs/cs/0212008</li> <li>Self-tuning spectral clustering, Zelnik-Manor, L., Perona, P, Advances in neural information processing systems, pp. 1601-1608, 2005, https://papers.nips.cc/paper/2619-self-tuning-spectral-clustering.pdf</li> <li>Adaptive kernel density-based anomaly detection for nonlinear systems, Zhang, L., Lin, J., Karim, R, Knowledge-Based Systems 139, 50-63, 2018, https://www.sciencedirect.com/science/article/pii/S0950705117304707</li> </ol>