Description: The Molgedey and Schuster decorrelation algorithm, having square mixing matrix and no noise . Truncation is used for the time shifted matrix, and it is forced to be symmetric . The delay Tau is estimated .
The number of independent components are calculated using Bayes Information Criterion (BIC), with PCA for dimension reduction.-The Molgedey and Schuster decorrelation algorithm, having square mixing matrix and no noise . Truncation is used for the time shifted matrix, and it is forced to be symmetric . The delay Tau is estimated . The number of independent components are calculated using Bayes Information Criterion (BIC), with PCA for dimension reduction. Platform: |
Size: 12169 |
Author:海心 |
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Description: he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization.
The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.
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Size: 7730 |
Author:薛耀斌 |
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Description: ICA算法The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.-ICA algorithm:The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction. Platform: |
Size: 563873 |
Author:陈互 |
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Description: ICA算法The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.-ICA algorithm:The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction. Platform: |
Size: 563200 |
Author:陈互 |
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Description: The Molgedey and Schuster decorrelation algorithm, having square mixing matrix and no noise . Truncation is used for the time shifted matrix, and it is forced to be symmetric . The delay Tau is estimated .
The number of independent components are calculated using Bayes Information Criterion (BIC), with PCA for dimension reduction.-The Molgedey and Schuster decorrelation algorithm, having square mixing matrix and no noise . Truncation is used for the time shifted matrix, and it is forced to be symmetric . The delay Tau is estimated . The number of independent components are calculated using Bayes Information Criterion (BIC), with PCA for dimension reduction. Platform: |
Size: 12288 |
Author:海心 |
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Description: he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization.
The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.
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Size: 7168 |
Author:薛耀斌 |
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Description: 一个用PCA和LDA降维，并用knn分类的人脸识别例程-A dimension reduction using PCA and LDA, and face recognition with the knn classification routines Platform: |
Size: 4860928 |
Author:taoda |
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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: 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: This paper develops an efficient classification algorithm called UDP, which reduces the high dimension of sample to low dimensional subspace simply said as dimensionality reduction.UDP takes into account both the local and non-local quantities. It can well characterize the local scatter as well non-local scatter which simultaneously maximizes the non–local scatter and minimizes the local scatter. This makes UDP more powerful and more intuitive than LDA and PCA. This makes UDP a good choice for real-world biometrics application The proposed method is applied to palm biometrics and is examined by small set of samples per class.
-This paper develops an efficient classification algorithm called UDP, which reduces the high dimension of sample to low dimensional subspace simply said as dimensionality reduction.UDP takes into account both the local and non-local quantities. It can well characterize the local scatter as well non-local scatter which simultaneously maximizes the non–local scatter and minimizes the local scatter. This makes UDP more powerful and more intuitive than LDA and PCA. This makes UDP a good choice for real-world biometrics application The proposed method is applied to palm biometrics and is examined by small set of samples per class.
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Size: 89088 |
Author:hello |
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Description: 利用Matlab编程实现主成分分析,从数学角度来看，这是一种降维处理技术-Using Matlab programming principal component analysis, from a mathematical point of view, this is a dimension reduction process technology Platform: |
Size: 31744 |
Author:李枫 |
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Description: 对人脸图片用PCA降维，提取特征脸，附上ORL数据库-dimension reduction using PCA to extract eigenfaces of face images and the ORL dataset will also be provided Platform: |
Size: 6640640 |
Author:houyifu |
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Description: 针对稀疏表示识别方法需要大量样本训练过完备字典且特征冗余度较高的问题，提出了结合过完备字典学习与PCA降维的小样本语音情感识别算法.该方法首先用PCA降维方法将特征降维，再将处理后的特征用于过完备字典训练与稀疏表示识别方法，从而给出了语音情感特征的稀疏表示方法，并确定了新算法的具体步骤.为验证其有效性，在同等特征维数下，将方法与BP, SVM进行比较，并对比、分析语音情感特征稀疏化前后对语音情感识别率、时间效率以及空间效率的影响.试验结果表明，所提出方法的识别率比SVM与BP高 与采用稀疏化前的特征相比，稀疏化后的特征向量更便于处理，平均识别率提高约15 ，时间效率提高近原来的1 /2，空间效率提升近原来的1 /3.
-Identification methods for sparse representation requires a lot of training samples and high over-complete dictionary feature redundancy problem, a combination of over-complete dictionary learning and PCA dimension small sample speech emotion recognition algorithms. Firstly, the PCA dimension reduction methods feature reduction, feature and then treatment for the over-complete dictionary training and recognition sparse representation, which gives a speech emotion feature sparse representation, and to determine the specific steps of the new algorithm. To verify its validity, in Under the same number of features, the method and BP, SVM compare and contrast, analyze the impact before and after the speech emotion feature sparse speech emotion recognition rate, time-efficient and space-efficient. experimental results show that the recognition rate of the proposed method than High SVM and BP compared to pre-thinning characteristics using eigenvectors easier after thinning processing, the av Platform: |
Size: 629760 |
Author:wangming |
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Description: this article describes the ideas behind of principal component analysis and uses that to reduce the dimension of a data space Platform: |
Size: 601088 |
Author:mamad |
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Description: 基于MATLAB平台的通过PCA降维后利用支持向量机SVM的0-9数字识别，适合初学者学习使用-The MATLAB platform by PCA after dimension reduction using 0-9 digital recognition with support vector machine based on SVM, suitable for beginners learning to use Platform: |
Size: 689152 |
Author:被遗忘的旧时光 |
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Description: 人脸搜索简单实现，使用主成分分析算法(PCA)，依赖opencv，对若干张人脸图片进行PCA降维处理，然后将输入人脸图片与降维后的数据做比较，根据权重输出结果，权重越大则人脸越相似-Simple face search, using the principal component analysis (PCA) algorithm, opencv, for a number of face images for PCA dimension reduction, and then the input image and reduce the dimension of the data to do a comparison, according to the weight of the results, the greater the weight of the face more similar Platform: |
Size: 1146880 |
Author:shishiteng |
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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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Search - dimension reduction using PCA