Ⅰ matlab中的降維函數是什麼
drttoolbox : Matlab Toolbox for Dimensionality Rection是Laurens van der Maaten數據降維的工具箱。
裡面囊括了幾乎所有的數據降維演算法：
- Principal Component Analysis ('PCA')
- Linear Discriminant Analysis ('LDA')
- Independent Component Analysis ('ICA')
- Multidimensional scaling ('MDS')
- Isomap ('Isomap')
- Landmark Isomap ('LandmarkIsomap')
- Locally Linear Embedding ('LLE')
- Locally Linear Coordination ('LLC')
- Laplacian Eigenmaps ('Laplacian')
- Hessian LLE ('HessianLLE')
- Local Tangent Space Alignment ('LTSA')
- Diffusion maps ('DiffusionMaps')
- Kernel PCA ('KernelPCA')
- Generalized Discriminant Analysis ('KernelLDA')
- Stochastic Neighbor Embedding ('SNE')
- Neighborhood Preserving Embedding ('NPE')
- Linearity Preserving Projection ('LPP')
- Stochastic Proximity Embedding ('SPE')
- Linear Local Tangent Space Alignment ('LLTSA')
- Simple PCA ('SPCA')
Ⅱ 降維工具箱的lda對訓練樣本和測試樣本一起運算嗎
我把訓練樣本和測試樣本的數據用PCA降維後,直接用歐式距離計算訓練向量和測試向量的距離,發現准確率一點都不比LDA差。LDA的主要優點是不是在於降維?