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empca2.tar
- 模式分类中应用到的PCA算法，包括其奇异值分解SVD算法。可用来降维提取主元素等。-pattern classification applied to the PCA algorithm, including its SVD singular value decomposition algorithm. Can be used to take down the main Viti Levu and other elements.
PCA_for_3_component_compound
- 该程序旨在模拟处理分析多种化学物质混合物的吸收光谱数据，提取出各个化学成分的峰值所在。该程序采用svd方法对光谱数据的协方差矩阵进行PCA分析，并且取得令人满意的效果。-The program designed to simulate the processing and analysis of multiple chemical mixtures of the absorption spectra of the data, extracting the chemical composition
Eigenfaces
- Eigenfaces tests for grayscale images using PCA and SVD
ComputeEigenfaces
- svd提取特征脸，Computer Vision课程作业，用PCA计算特征脸，提取主成分，用PCA系数进行相似度计算。-comupute face similarity using eigenface and PCA analysis
CSE_Assignment3
- I am submitting my Computational science and engineering assignment #3 based on SVD/PCA.The zip file contains matlab codes of Exercise no:1 and Exercise no:3 as program_1.m and program_3.m files respectively. I have made a separate reports for
Face-Recognition
- Face Recognition By PCA using SVD technique .
KL_SVD_face_recognition
- PCA主成分分析，采用KL投影和SVD分解提取人脸特征向量，最后采用最近邻判别法计算识别率。-Face recognition based on PCA. KL projection and SVD are used to extract face eigenvectors. Recognition rate is calculated by k nearest neighbors(KNN) method.
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
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.
python-code-for-Machine-learning
- 用于机器学习的全方位python代码，包括K-近邻算法、决策树、朴素贝叶斯、Logistic 回归 、支持向量机、利用 AdaBoost 元算法提高分类性能、预测数值型数据：回归、树回归、利用 K-均值聚类算法对未标注数据分组、使用 Apriori 算法进行关联分析、使用 FP-growth 算法来高效分析频繁项集、利用 PCA 来简化数据、利用 SVD 简化数据、大数据与 MapReduce-The full range of python code for machine learning
jiangwei
- pca、svd、mds 数据降维，用来对数据进行预处理 新人学习，叨扰(pca data dimension reduction)
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)