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主成分分析，主成分分析原理介绍及简单应用-Principle component analysis
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Principle component analysis PCA
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主分量分析，用于对脑电信号的判别分析，也可用于其他的二分类分析。-the classifier of principle component analysis,using for EEG and binary classification
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利用主成分分析和K-means聚类实现聚类的Matlab算法-principle component analysis and then use k-means clustering algorithm to realize the clustering
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PCA和ICA方法实现自己编的pca和IHS融合代码!! 与大家交流!
-principle component analysis
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face recognition by using principle component analysis
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模式识别理论中KPCA(Kernel based Principle Component Analysis)方法的推导-Kernel based Principle Component Analysis-pattern recognition
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principle component analysis is powerfull matlab source for face recognition.
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运用matlab编程实现PCA主成分分析算法-Principle component analysis
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PCA Principle component analysis code
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通过在线PCA来识别手势，并能对新增手势做一定的增量，很好的实现了增量学习。算法具有效率高，识别率好，速度快等特点，同样适合其他模式识别方面的应用。-The learning method for hand gesture recognition system based on vision is commonly off-line，which results in repeated off-line learning when new hand gestures come． Its real-
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Function to perform Principle Component Analysis over a set of training
vectors passed as a concatenated matrix.
Usage:- [V,D,M] = pca(X,n)
[V,D] = pca(X,aM,n)
where:-
<input>
X = concatenated set of column vectors
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稀疏主成分分析算法，用于特征提取，维数约简。-Sparse Principle Component Analysis for feature extraction
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This code use Principle Component Analysis(PCA)for feature extraction
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This function used to calculate PCA of image.
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General Principle Component Analysis (GPCA)
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比较深入的分析了PCA人脸识别方法的原理，并对PCA在应用过程中遇到的特征值选择和距离准则问题进行了研究，实现了基于PCA算法的人脸识别。
-First, the thesis investigates principle component analysis (PCA) approachdeeply, and then the choice of feature vector of sample s covariance matrix anddistance measure criteri
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A matlab for principle component analysis
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principle component analysis is one of most powerful method for feather extraction
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主成分分析(Principle Component Analysis, PCA)是最为常用的特征提取方法，被广泛应用到各领域，如图像处理、综合评价、语音识别、故障诊断等。-Principal component analysis (Principle Component Analysis, PCA) is the most commonly used feature extraction methods are widely applied to various fields, such as
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