K-Means PCA降维
所属分类：图形图像处理
开发工具：matlab
文件大小：33KB
下载次数：18
上传日期：2017-06-22 22:05:55
上 传 者：
赵嘉慧
说明： K-Means算法,不要求建立模型之后对结果进行新的预测，没有相应的标签，只是根据数据的特征对数据进行聚类。主成分分析降维对数据进行可视化操作,对features进行降维.
(K-Means algorithm does not require the establishment of the model after the new prediction of the results, there is no corresponding tag, but only on the characteristics of data clustering data. The principal component analysis reduces the dimension, carries on the visualization operation to the data, reduces the dimension to the features.)
文件列表:
Homework3\computeCentroids.m (1207, 2011-11-30)
Homework3\displayData.m (1502, 2011-11-30)
Homework3\drawLine.m (232, 2011-11-30)
Homework3\ex3.m (3037, 2017-06-08)
Homework3\ex3data1.mat (995, 2011-11-30)
Homework3\ex3data2.mat (4784, 2011-11-30)
Homework3\ex3_pca.m (3177, 2017-06-08)
Homework3\featureNormalize.m (510, 2011-11-30)
Homework3\findClosestCentroids.m (994, 2011-11-30)
Homework3\Homework3.docx (22536, 2017-06-08)
Homework3\kMeansInitCentroids.m (602, 2011-11-30)
Homework3\pca.m (842, 2011-11-30)
Homework3\plotDataPoints.m (434, 2011-11-30)
Homework3\plotProgresskMeans.m (840, 2011-11-30)
Homework3\projectData.m (928, 2011-11-30)
Homework3\recoverData.m (983, 2011-11-30)
Homework3\runkMeans.m (1974, 2011-11-30)
Homework3\~$mework3.docx (162, 2017-06-22)
Homework3 (0, 2017-06-22)
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