搜索资源列表
fum
- 标准化后进行PCA特征提取，然后聚类分类-After standardized PCA feature extraction, clustering and classification
qenban
- 基于K均值的PSO聚类算法,包含优化类的几个简单示例程序，Gabor小波变换与PCA的人脸识别代码。- K-means clustering algorithm based on the PSO, Optimization class contains several simple sample programs, Gabor wavelet transform and PCA face recognition code.
jaomie
- 一个很有用的程序，基于K均值的PSO聚类算法,借鉴了主成分分析算法（PCA）。- A very useful program, K-means clustering algorithm based on the PSO, It draws on principal component analysis algorithm (PCA).
leiling_v80
- 利用最小二乘法进行拟合多元非线性方程，借鉴了主成分分析算法（PCA），用MATLAB实现动态聚类或迭代自组织数据分析。- Multivariate least squares fitting method of nonlinear equations, It draws on principal component analysis algorithm (PCA), Using MATLAB dynamic clustering or iterative self-organizing data
faipun_V8.3
- 用MATLAB实现动态聚类或迭代自组织数据分析，搭建OFDM通信系统的框架，借鉴了主成分分析算法（PCA）。- Using MATLAB dynamic clustering or iterative self-organizing da
niumai_v25
- 最终的权值矩阵就是滤波器的系数，基于欧几里得距离的聚类分析，Gabor小波变换与PCA的人脸识别代码。- The final weight matrix is ??the filter coefficient, Clustering analysis based on Euclidean distance, Gabor wavelet transform and PCA face recognition code.
kuntai
- Gabor小波变换与PCA的人脸识别代码，基于K均值的PSO聚类算法,使用大量的有限元法求解偏微分方程。- Gabor wavelet transform and PCA face recognition code, K-means clustering algorithm based on the PSO, Using a large number of finite element method to solve partial differential equations.
pca_kmeans_VQ
- 基于PCA+k-means聚类的语音识别算法，参数用LPCC+MFCC以及动态参数，识别率高已发SCI论文-Based on PCA+ k-means clustering speech recognition algorithm, parameters and dynamic parameters LPCC+ MFCC recognition rate sent SCI papers
Clustering-PCA
- 主成分分析（Principal Component Analysis，PCA）， 是一种统计方法。通过正交变换将一组可能存在相关性的变量转换为一组线性不相关的变量，转换后的这组变量叫主成分。-Principal component analysis is a statistical method. A set of variables that may be related to a set of variables that may be correlated to a set of vari
fengfai
- 结合PCA的尺度不变特征变换(SIFT)算法，音频信号通过LM386放大，可实现对二维数据的聚类。- Combined with PCA scale invariant feature transform (SIFT) algorithm, LM386 audio signal amplification, Can realize the two-dimensional data clustering.
taohan_v18
- 用MATLAB实现动态聚类或迭代自组织数据分析，包含光伏电池模块、MPPT模块、BOOST模块、逆变模块，是学习PCA特征提取的很好的学习资料。- Using MATLAB dynamic clustering or iterative self-organizing data analysis, PV modules contain, MPPT module, BOOST module, inverter module, Is a good learning materials to lear
hbgyy
- 利用自然梯度算法，Gabor小波变换与PCA的人脸识别代码，可实现对二维数据的聚类。- Use of natural gradient algorithm, Gabor wavelet transform and PCA face recognition code, Can realize the two-dimensional data clustering.
suiheigun
- Clustering analysis based on Euclidean distance, Gabor wavelet transform and PCA face recognition code, Can dynamically adjust the parameters of the operating environment.
jenyen-V4.6
- Using MATLAB dynamic clustering or iterative self-organizing data analysis, Independent component analysis for image processing, Is a good learning materials to learn PCA feature extraction.
ML
- GMM高斯混合模型EM算法聚类，PCA主成分分析，以及从人脸图像中提取主成分(GMM Gauss hybrid model EM algorithm clustering, PCA principal component analysis, and extraction of principal components from face images)
kernel_eca-master
- Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010.(We introduce kernel entropy component analysis (kernel ECA) as a new method fo
EnglishChuLi
- 利用python编写的文本预处理的程序，包含了每一步的实现代码，分为删除标点符号、删除停用词、相似度计算、PCA降维、聚类以及可视化等，运行环境为pytharm，python3开发环境(The text preprocessing program written by Python contains every step of implementation code, which is divided into delete punctuation marks, delete stop word
ChineseChuLi
- 中文文本处理的python程序，包括分词、删除特殊字符、删除停用词、爬虫程序、PCA降维、Kmean聚类、可视化等(Python programs for Chinese text processing, including participle, deleting special characters, deleting disuse words, crawler programs, PCA dimensionality reduction, Kmean clustering, visuali
PCA,KPCA完整程序
- 降维，用作聚类算法使用。具有很好效果，可以用作图像去噪(Dimensionality reduction is used as a clustering algorithm. It has good effect and can be used for image denoising.)
04825210PCA1
- 对碰撞信号特征进行降维和聚类分析，提高分类精度(Reducing dimension and clustering analysis of collision signal features to improve classification accuracy)