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20140222 Tokyo.R#36 RでSPADEとviSNEを使って次元削減と可視化
- 5. 前回のTokyo.R#35で
Cyto Spanning tree Progression of Density normalized Events (SPADE)
n次元定量データのパターンから分化系統樹作成
新規
退会
課金厨
無課金厨
重課金厨
Nat Biotechnol. 2011 Oct 2;29(10):886-91
Science. 2011 May 6;332(6030):687-96
http://d.hatena.ne.jp/MikuHatsune/20130922
- 6. 次元削減法
線形
K-nearest neighbors algorithm (kNN)
principal component analysis (PCA)
linear discriminant analysis (LDA)
canonical correlation analysis (CCA)
feature vectors
非線形
Sammon's mapping
Self-organizing map
Principal curves and manifolds
Autoencoders
Gaussian process latent variable models
Curvilinear component analysis
Curvilinear distance analysis
Diffeomorphic dimensionality reduction
Kernel principal component analysis
Isomap
Locally-linear embedding(LLE)
Laplacian eigenmaps
Manifold alignment
Diffusion maps
Hessian LLE, Modified LLE
Local tangent space alignment
Local multidimensional scaling
Maximum variance unfolding
Nonlinear PCA
Data-driven high-dimensional scaling
Manifold sculpting
RankVisu
Topologically constrained isometric
embedding
Relational perspective map