Description: 维数约简工具箱源代码，包括PCA、LLE等学习算法，可用于模式识别、数据挖掘和统计分析等。-dimension reduction toolkit source code, including the PCA, LLE and other learning algorithms can be used for pattern recognition, data mining and statistical analysis. Platform: |
Size: 194560 |
Author:王为 |
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Description: 做模式分类时（例如指纹识别，人脸识别），一个需要处理的难题是维数非常大，人脸往往是百万维的，目前计算机的能力还不足以快速地计算这么高维的数据。pca是一中降维的方法，用它可以把高维数据映射到一个维数较低的空间上考虑。-To do pattern classification (for example, fingerprint identification, face recognition), a need to deal with the problem is very large dimension, often millions of people face-dimensional, the current capacity of the computer fast enough to calculate such a high-dimensional data. PCA is a dimensionality reduction of the method, it can be high dimensional data is mapped to a lower dimension space to consider. Platform: |
Size: 1024 |
Author:李志焕 |
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Description: 2DPCA新的降维方法,是PCA的改进,新,很值得一看!-2DPCA new dimension reduction method is PCA improvements, the new, it is worth a visit! Platform: |
Size: 1024 |
Author:黄传波 |
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Description: 人脸识别的经典算法的完美结合，PAC与FISHER算法C++实现，首先通过PCA进行维数约简，然后通过FISHER进行最有利的方向投影。识别效率是所有监督学习的上限。-Face Recognition Algorithm for the perfect combination of classic, PAC and FISHER algorithm C++ Realize, first of all carried out through the PCA dimension reduction, and then through to the most favorable FISHER projection direction. Recognition efficiency is all supervised learning ceiling. Platform: |
Size: 758784 |
Author:NEO |
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Description: This toolbox is an educational and recreative toolbox around recent ideas in the field of dimension reduction.
* PCA : classical Principal Componnent Analysis (linear projection).
* Nonlinear dimensionality reduction by locally linear embedding.
* Laplacian Eigenmaps for dimensionality reduction and data representation-This toolbox is an educational and recreative toolbox around recent ideas in the field of dimension reduction.
* PCA : classical Principal Componnent Analysis (linear projection).
* Nonlinear dimensionality reduction by locally linear embedding.
* Laplacian Eigenmaps for dimensionality reduction and data representation Platform: |
Size: 226304 |
Author:tra ba huy |
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Description: 快速PCA计算方法，有效实现降维等操作，和特征选择-Fast PCA method of calculation of effective dimension reduction and other operations, and feature selection Platform: |
Size: 1024 |
Author:anan |
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Description: MATLAB源程序，主成分分析法（PCA），这是一种降维的方法，可以用于多通道数字信号处理-MATLAB source code, principal component analysis (PCA), which is a dimension reduction method can be used for multi-channel digital signal processing Platform: |
Size: 1024 |
Author:qsylj |
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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: PCA+LDA人脸识别，识别率高于单独PCA或LDA算法。需要matlab dimension reducation toolbox。-Face verification using PCA and LDA fusion. Better performance than single PCA or LDA algorithm. The image database is included. Matlab dimension reduction toolbox is requrired. Platform: |
Size: 1975296 |
Author:taiji |
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Description: 该工具箱中包含了多种降维算法。其中有传统的PCA和Local PCA算法，也有典型的流形学习算法，如Isomap、LLE、HLLE、Laplacian Eigenmaps 和 Local Tangent Space 。-The toolbox contains a variety of dimensionality reduction algorithms. In which the traditional PCA and Local PCA algorithms, there are the typical manifold learning algorithms such as Isomap, LLE, HLLE, Laplacian Eigenmaps and Local Tangent Space. Platform: |
Size: 195584 |
Author:芝麻 |
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Description: 光谱空间的主成分分析 PCA ：旨在利用降维的思想，把多指标转化为少数几个综合标。-Principal component analysis of spectral space PCA: dimension reduction ideas to use to index into a few more comprehensive standard. Platform: |
Size: 2921472 |
Author:红 |
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Description: pca特征向量提取 利用pca的方法获取特征植及特征向量 最后可以自己根据需要降维-pca pca feature vector extraction method using characteristics of plants and to obtain the final feature vector dimension reduction can be their own as needed Platform: |
Size: 4096 |
Author:张天号 |
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Description: pca降维 在pca提取vd后可以利用降维进行更加简便的操作-pca pca extraction vd dimension reduction in the dimensionality reduction can be used after a more simple operation Platform: |
Size: 57344 |
Author:张天号 |
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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.
Platform: |
Size: 94208 |
Author:yumingwei |
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