搜索资源列表
k-means
- K均值算法，将数据矩阵命名为data，设置聚类簇个数k，可对多维数据进行聚类。-K mean algorithm, the data matrix is named data, set the number of clusters K, can be used to cluster the multi-dimensional data.
mvrvm_c
- 相关向量机RVM用于预测和分类,含有归一化，训练和测试，可以多变量输出，解决海瑟矩阵问题-RVM relevance vector machine for prediction and classification, containing normalization, training and testing, can be more variable output matrix problem solving Heather
pmf
- 推荐系统 概率矩阵分解代码 内部已包含有数据集，已分为训练集与测试集-Recommended system probability matrix decomposition Code
pujulei
- 谱聚类算法建立在谱图理论基础上，与传统的聚类算法相比，它具有能在任意形状的样本空间上聚类且收敛于全局最优解的优点。 该算法首先根据给定的样本数据集定义一个描述成对数据点相似度的亲合矩阵,并且计算矩阵的特征值和特征向量 ， 然后选择合适 的特征向量聚类不同的数据点。谱聚类算法最初用于计算机视觉 、VLS I 设计等领域， 最近才开始用于机器学习中，并迅速成为国际上机器学习领域的研究热点。-Spectral clustering algorithm based on the spectrum b
k_clique
- [X,Y,Z] = k_clique(k,A) Inputs: k - clique size A - adjacency matrix Outputs: X - detected communities Y - all cliques (i.e. complete subgraphs that are not parts of larger complete subgraphs) Z - k-clique matrix-k-clique alg
NMF
- 用matlab实现的基于非负矩阵分解NMF的聚类算法，已测试通过-NMF decomposition using clustering algorithm based on non-negative matrix matlab achieved, have been tested
code
- 完成三个聚类算法：k-means，非负矩阵分解，谱聚类-Please implement three clustering algorithms: k-means, clustering by non-negative matrix factorization (NMF), and spectral clustering.
datato1ofm
- Take categorical data matrix and transform whole matrix to binary sparse 1ofM matrix, keeping track of what came where. Ideal for any form of count-based probabilistic analysis.-Take categorical data matrix and transform whole matrix to binary sparse
kde2d
- 二维高斯核函数重构 重构方法不依赖于参数化模型-2D Gaussian Kernel Reconstruction fast and accurate state-of-the-art bivariate kernel density estimator with diagonal bandwidth matrix. The kernel is assumed to be Gaussian. The two bandwidth parameter
svd
- 奇异值分解在某些方面与对称矩阵或厄米矩阵基于特征向量的对角化类似。然而这两种矩阵分解尽管有其相关性，但还是有明显的不同。-Singular value decomposition in some respects symmetric matrix or Hermitian matrix based on a similar feature vectors diagonalization. However, the two matrix decomposition in spite of its
AprioriCpuFlexarry
- 基于位表矩阵的Apriori算法，可以大幅缩小算法存储空间，减少读取文件次数。 测试文件格式为 “0 1 2 3 4 5 6 7 8 9 10 ”-Bitmap matrix of Apriori algorithm, the algorithm can significantly reduce storage space and reduce the number of times to read the file. Test file format " 012
kernelpca_tutorial
- 基于R语言的kernel矩阵生成代码，已包含高斯核，可以修改核函数-R language based on the kernel matrix generation code, has included the Gauss kernel, you can modify the kernel function
code_BPMF
- 如何使它工作： 1。创建一个单独的目录，并将所有这些文件下载到相同的目录中 2。下载7个文件： *demo:主文件demo:PMF和贝叶斯PMF * PMF.m:训练的PMF模型 * bayespmf.m贝叶斯PMF模型实现吉布斯采样器。 * moviedata.mat样本数据包含三元组（user_id，movie_id，评分） * makematrix.m：辅助功能转换成大型矩阵的三元组。 * PRED.m:辅助功能使得预测验证集。 三.在Matlab只需运
FJIR.R
- 模糊聚类分析 - 建立模糊相似矩阵，以R语言实现。-build a fuzzy matrix
lasso
- 使用lasso方法，对特征矩阵进行优化特征选择（向量选择），使其达到最优。-Use lasso method, optimize the characteristic matrix feature selection (vector selection), to the optimal.
F_Score
- 特征选择的一种方法，对数据矩阵进行f打分，然后降序排序，可以从中发现重要的数据。-A method of feature selection, to f score data matrix, and then descending order, you can find important data.
实验6_NMF-ICA
- 实现非负矩阵分解算法和独立成分分析，得到遥感遥感图像解混结果(The non negative matrix factorization algorithm and independent component analysis are implemented to get the unmixing results of remote sensing images)
cc
- 可以执行矩阵的相关性子矩阵挖掘，代码开始部分的备注里包含实例矩阵，大家可以实验看看，代码原创，实验可以，但是如果用在商业或者学术里，请和我联系~(Relative matrix mining of matrices can be performed)
appendorganizehead
- This matrix C++ template class library is for performing comm()
FM algorithm
- 因子分解机( FM)算法是一种基于矩阵分解的机器学习算法，是一种常用的推荐算法。(Factorization algorithm is a matrix-based machine learning algorithm, which is a commonly used recommendation algorithm.)