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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 ar
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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
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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
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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
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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
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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.
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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
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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 ca
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Dimension reduction using principal component analysis.
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这是一个很好的人脸识别的程序，首先用PCA降维，然后用LDA方法进行分类，用的是ORL人脸库。-This is a good face recognition program, the first PCA dimension reduction, classification, and then using the LDA method, use the ORL face database.
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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
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人脸检测一直是人们在研究的问题，流形学习用于人脸检测中的特征提取，用PCA与constructM进行降维，KNN分类器用于分类。取得非常好的效果。-Face detection has been the problem of people in the study, manifold learning for face detection feature extraction using PCA and constructM dimension reduction, KNN classifier
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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
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使用matlab进行仿真，还有pca降维，kl变换方法进行人脸识别的参考论文-Using MATLAB simulation, and the PCA dimension reduction, the KL transform method for face recognition reference papers
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this article describes the ideas behind of principal component analysis and uses that to reduce the dimension of a data space
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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
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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
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实现了主元分析（PCA）和独立分量分析（ICA）相关信号处理。非线性降维。(Implements Principal Component Analysis (PCA) and Independent Component Analysis (ICA) correlation signal. Non-linear dimension reduction using kernel PCA.)
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在信号处理技术中，使用PCA非线性降维。(Non-linear dimension reduction using kernel PCA.)
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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)
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