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线性判别分析（LDA）用于特征选择，可以对数据集或者图像提取有用特征，用于分类或者聚类等机器学习应用中-Linear Discriminant Analysis (LDA) for feature selection, application in dataset or image feature extraction, for classification or clustering applications in machine learning
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K-meansK均值聚类在无监督的情况下选择图像特征的算法-K-meansK means clustering in the case of unsupervised image feature selection algorithm
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describes selforganizing feature maps which describes the clustering and compression
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图像特征提取的总结，用MATLAB模糊聚类算法进行图像分割，阀值分割及特征提取的资料和作业。-Summary of the image feature extraction, fuzzy clustering algorithm using MATLAB for image segmentation, threshold segmentation and feature extraction of data and operations.
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针对FCM算法的运行时间长和计算量大的问题，提出了改进的FCM算法，先将图像分割成窗口大小的子块，然后以子块为单位提取特征向量，用FCM聚类粗分割，然后对边缘子块，以像素为单位从新提取特征向量，进行细分割。分割后的结果提高了运行速度和分割精度。-For the FCM algorithm and the calculation of long run the problem of large proposed improved FCM algorithm, first image into bl
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本程序实现了Matlab 基于SOFM（自组织特征映射神经元网络）颜色聚类图像分割。-This application implements the Matlab based on SOFM (self-organizing feature map neural network) color clustering image segmentation.
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其中包括颜色矩特征提取，四层小波特征提取以及kmeans聚类算法，Matlab编程实现，希望对学习有帮助-Including the extraction of color moment feature, the four layer wavelet feature extraction and kmeans clustering algorithm, Matlab programming, and they hope to help with learning
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Matlab实现BOVW模型，特征提取采用SIFT算法，字典学习采用k-means聚类学习，数据集采用UCM21类分类信息-Matlab achieve BOVW model, feature extraction algorithm using SIFT, dictionary learning using k-means clustering, data collection using UCM21 class category
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谱聚类能够识别任意形状的样本空间且收敛于全局最优解，其基本思想是利用样本数据相似矩阵的进行特征分解后得到的特征向量进行聚类，程序进行了几种不同聚类算法的比较，包括Q矩阵聚类，kmeans聚类，第一特征分量聚类，第二广义特征分量聚类，公用数据生成和近邻矩阵生成(Spectral clustering can distinguish arbitrary sample space and converge to the global optimal solution, the basic idea i
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