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pythonsrc
- 机器学习算法，包括主成分分析方法，奇异值分解，逻辑回归，最小二乘法线性回归，朴素贝叶斯-machine learning algorithm prototype including PCA, SVD, Logic Regression, LMS and Naive Bayes
dimensionality-reduction-and-k-means
- 1.使用k-svd对数据进行稀疏表示，降维 2.使用k-means对上述数据聚类-1.use k-svd to reduce the dimensions of data 2.clster the data by k-means
SVD_EST
- 自行编写的加入噪声估计的KSVD去噪算法，利用SVD分解进行噪声估计- KSVD denoising algorithm to prepare the addition of noise estimation, the use of SVD decomposition noise estimation
svd
- 奇异值分解在某些方面与对称矩阵或厄米矩阵基于特征向量的对角化类似。然而这两种矩阵分解尽管有其相关性，但还是有明显的不同。-Singular value decomposition in some respects symmetric matrix or Hermitian matrix based on a similar feature vectors diagonalization. However, the two matrix decomposition in spite of its
mySVD
- svd算法可用于降维，也可用于pca的分解中。-SVD algorithm can be used to complete the PCA algorithm. It can also be used to realize dimensionality reduction.
SVD.m
- 利用SVD实现item-based CF: 优点： 简化数据，去除噪声，提高算法的结果 缺点： 数据的转换可能难以理解 适用数据类型： 数值型数据(Svd decomposition plays an important role in the decomposition of eigenvalues of high-dimensional data, while using low-dimensional data for approximate approximation)
ukawwo
- SVD算法：利用SVD分解的平移,旋转矩阵算法()
27016265
- SVD算法：利用SVD分解的平移,旋转矩阵算法()
降维code
- 了解降维、特征筛选等基本原理 掌握PCA、SVD、LAD和NMF等算法实现及应用(Understand the basic principles of dimensionality reduction and feature selection Master the algorithm implementation and application of PCA, SVD, lad and NMF)