Description: 程序说明：y = pca(mixedsig)，程序中mixedsig为 n*T 阶混合数据矩阵，n为信号个数，T为采样点数。y为 m*T 阶主分量矩阵。n是维数，T是样本数。-Procedure Note: y = pca (mixedsig), the program mixedsig for the n* T-order mixed data matrix, n is the signal number, T the sampling points. y for m* T-order principal component matrix. n is the dimension, T is the number of samples. Platform: |
Size: 1024 |
Author:王辉 |
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Description: 用pca 和 lda 实现数据的降维,加快机器的特征提取的速度。-Pca and lda of data with dimension reduction, feature extraction to speed up the speed of the machine. Platform: |
Size: 3072 |
Author:江红 |
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Description: This an efficient implementation of PCA, which use smaller dimension of the data matrix to compute the eigenvectors-This is an efficient implementation of PCA, which use smaller dimension of the data matrix to compute the eigenvectors Platform: |
Size: 1024 |
Author:samsom |
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Description: PCA技术的一大好处是对数据进行降维的处理。我们可以对新求出的“主元”向量的重要性进行排序，根据需要取前面最重要的部分，将后面的维数省去，可以达到降维从而简化模型或是对数据进行压缩的效果。同时最大程度的保持了原有数据的信息。-A major advantage of PCA technology is reduce the dimension of the data processing. We can calculate the new " principal component" vector order of importance, according to need to take the most important part of the front, eliminating the rear of the dimensions, dimension reduction can be achieved to simplify the model or the effect of data compression . At the same time maintaining the greatest degree of the original data. Platform: |
Size: 1024 |
Author:xyhuang |
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Description: The Principal component analysis, is a standard technique used for data reduction in statistical pattern recognition and signal processing
A common problem in statistical pattern recognition is feature selection or feature extraction. Feature selection is a process whereby a data space is transformed into a feature space that theory has exactly same dimension as the original data space. However the transformation is designed in such a way that the data set is represented by a reduced number of “effective features” and most of the intrinsic information content of the data or the data set undergoes a dimensionality reduction.
PCA Platform: |
Size: 13312 |
Author:binu |
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Description: 模式识别作业-完全自编仿真程序。先用PCA对IRIS数据集进行降维，然后用最小错误法对降维的数据进行分类。压缩包中既包括matlab源代码，又有自己写的报告，还有.MAT格式的IRIS数据集用作程序调用。程序有详细注释，很容易懂。最后结果输出到txt文件中。-Pattern recognition operations- completely self simulation program. First on the IRIS data set with PCA dimension reduction, and then with the minimum error method to classify the data dimension reduction. Both compressed package matlab source code, but also write their own reports, as well. MAT format, the program calls for IRIS data set. Procedures detailed notes, it is easy to understand. Finally, the output to a txt file.
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Size: 94208 |
Author:yumingwei |
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Description: 主成分分析，一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题，主要用于数据降维-PCA，a kind of statistical analysis method which master the main self-contradict of things, it can resolve the main factors from multiple things reveal the nature of things, simplify the complex problems,and it mainly used for data dimension reduction. Platform: |
Size: 3072 |
Author:QSJ |
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Description: pca主成分分析法 自己编写的 用于数据降维 减少数据分析时的计算量和计算时间-Pca principal component analysis their writing for data dimension reduction reduce data analysis calculation and computing time
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Size: 1024 |
Author:Cyy |
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Description: matlab下编译的pca算法进行数据减维程序，适合初学者使用-Pca algorithm for data dimension reduction, suitable for beginners to use
Platform: |
Size: 13312 |
Author:于于 |
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Description: 可以很好的实现流形学习算法中的线性降维算法PCA数据降维。-Can well realize the manifold learning algorithm of linear dimension reduction algorithm of PCA data dimension reduction. Platform: |
Size: 1024 |
Author:wy |
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Description: 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.) Platform: |
Size: 33792 |
Author:赵嘉慧
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Description: pca是主成分分析，提取特征，对数据进行降维处理(PCA is principal component analysis, which extracts features and processes the data in reduced dimension) Platform: |
Size: 1024 |
Author:margin_li
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Description: 再用主成分分析方法进行信号噪声去除。文件中包含三个子函数：1.Normalize_InputData，规则化输入数据；2.PCA_Reduce_Dimension，实现数据降维处理；3.PCA_Filter_Noise，实现噪声去除(the principal component analysis method for signal noise removal. The file contains three sub-functions: 1.Normalize_InputData, regularized input data; 2.PCA_Reduce_Dimension, to achieve data dimension reduction processing; 3.PCA_Filter_Noise, to achieve noise removal) Platform: |
Size: 1024 |
Author:ZonyShi
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Description: 这是吴恩达在course公开课上讲的数据降维的作业的代码，主要是应用PCA对数据降维(This is Wu Enda in the course open class lectures on data dimension reduction operations code, mainly using PCA for data dimensionality reduction) Platform: |
Size: 10718208 |
Author:`你管
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