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Description: 流形学习算法LTSA的线性化方法，在基因分类聚类中得到了应用，可以将新样本线性地投射到低维空间。-LTSA manifold learning algorithm of the linearization method, clustering in gene classification has been applied to new samples can be projected onto the linear and low-dimensional space.
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Size: 2048 |
Author: 仲国强 |
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Description: Matlab针对各种数据预处理的降维方法，源码集合。-Currently, the Matlab Toolbox for Dimensionality Reduction contains the following techniques:
Principal Component Analysis (PCA)
Probabilistic PCA
Factor Analysis (FA)
Sammon mapping
Linear Discriminant Analysis (LDA)
Multidimensional scaling (MDS)
Isomap
Landmark Isomap
Local Linear Embedding (LLE)
Laplacian Eigenmaps
Hessian LLE
Local Tangent Space Alignment (LTSA)
Conformal Eigenmaps (extension of LLE)
Maximum Variance Unfolding (extension of LLE)
Landmark MVU (LandmarkMVU)
Fast Maximum Variance Unfolding (FastMVU)
Kernel PCA
Generalized Discriminant Analysis (GDA)
Diffusion maps
Stochastic Neighbor Embedding (SNE)
Symmetric SNE (SymSNE)
new: t-Distributed Stochastic Neighbor Embedding (t-SNE)
Neighborhood Preserving Embedding (NPE)
Locality Preserving Projection (LPP)
Linear Local Tangent Space Alignment (LLTSA)
Stochastic Proximity Embedding (SPE)
Mu
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Size: 2029568 |
Author: jdzsj |
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Description: 该压缩文件为部分数据降维方法，有LTSA、HHLLE、ISOMAP、LLTSA、LLP-The compressed file for the partial data dimensionality reduction method, there are LTSA, HHLLE, ISOMAP, LLTSA, LLP
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Size: 7168 |
Author: 叶绪丹 |
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Description: 这个是KPCA核主成分分析的代码，好用，里面也带有范例(This is the KPCA kernel principal component analysis code, which is easy to use and also contains examples.)
Platform: |
Size: 6144 |
Author: TomloveJerry |
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