pca

I have several large rasters that I want to process in a PCA (to produce summary rasters). I have seen several examples whereby people seem to be simply calling prcomp or princomp. However, when I do this, I get the following error message: Error in as.vector(data): no method for coercing this S4 class to a vector Example code: files<-list.files() # a set of rasters layers<-stack(files) # using the raster package pca<-prcomp(layers) I have tried using a raster brick instead of stack but that doesn't seem to the issue. What method do I need to provide the command so that it can convert the

I have computed PCA using the following : function [signals,V] = pca2(data) [M,N] = size(data); data = reshape(data, M*N,1); % subtract off the mean for each dimension mn = mean(data,2); data = bsxfun(@minus, data, mean(data,1)); % construct the matrix Y Y = data'*data / (M*N-1); [V D] = eigs(Y, 10); % reduce to 10 dimension % project the original data signals = data * V; My question is: Is "signals" is the projection of the training set into the eigenspace? I saw in "Amir Hossein" code that "centered image vectors" that is "data" in the above code needs to be projected into the "facespace" by

I've got a rather large dataset that I would like to decompose but is too big to load into memory. Researching my options, it seems that sklearn's IncrementalPCA is a good choice, but I can't quite figure out how to make it work. I can load in the data just fine: f = h5py.File('my_big_data.h5') features = f['data'] And from this example , it seems I need to decide what size chunks I want to read from it: num_rows = data.shape[0] # total number of rows in data chunk_size = 10 # how many rows at a time to feed ipca Then I can create my IncrementalPCA, stream the data chunk-by-chunk, and

PCA的概念： 主要思想是将n维特征映射到k维上，这k维是全新的正交特征，这k维特征被称为主成分，在原数据的基础上重新构造出来k维。就是从原始的空间顺序的找出一组相互正交的坐标轴，新坐标轴的选择和数据本身有很大的关系。其中，第一个坐标轴是从原数据中方差最大的方向，第二个新坐标轴选择是与第一个坐标轴正交平面中使得方差最大的，第三个轴是与第一二轴正交的平面中方差最大的，依次类推。依次类推，可以得到n个这样的坐标轴。通过这种方式获得的新的坐标轴，我们发现，大部分方差都包含在前面k个坐标轴中，后面的坐标轴所含的方差几乎为0。于是，我们可以忽略余下的坐标轴，只保留前面k个含有绝大部分方差的坐标轴。事实上，这相当于只保留包含绝大部分方差的维度特征，而忽略包含方差几乎为0的特征维度，实现对数据特征的降维处理。 PCA算法： 优点：降低数据的复杂性，识别最重要的多个特征 缺点：不一定需要， 可能损失有用信息 适用数据类型：数值型数据 数据集下载链接： http://archive.ics.uci.edu/ml/machine-learning-databases/ 在PCA中应用的数据集： http://archive.ics.uci.edu/ml/machine-learning-databases/ secom/ （1）打开数据集计算特征数目：（列为特征数）在secom数据集中一行代表一条数据

I am using PCA to find out which variables in my dataset are redundand due to being highly correlated with other variables. I am using princomp matlab function on the data previously normalized using zscore: [coeff, PC, eigenvalues] = princomp(zscore(x)) I know that eigenvalues tell me how much variation of the dataset covers every principal component, and that coeff tells me how much of i-th original variable is in the j-th principal component (where i - rows, j - columns). So I assumed that to find out which variables out of the original dataset are the most important and which are the least

ISODATA聚类算法的matlab程序 作者：凯鲁嘎吉 - 博客园 http://www.cnblogs.com/kailugaji/ 参考： Kmeans及ISODATA算法的matlab实现 数据见： MATLAB实例：PCA降维 中的iris数据集，保存为：iris.data，最后一列是类标签。 demo_isodata.m clear clc data_load=dlmread('iris.data'); [~,dim]=size(data_load); x=data_load(:,1:dim-1); K=3; theta_N=1; theta_S=1; theta_c=4; L=1; I=5; ISODATA(x,K,theta_N,theta_S,theta_c,L,I) ISODATA.m function ISODATA(x,K,theta_N,theta_S,theta_c,L,I) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%input parameters%%%%%% % x : data % K : 预期的聚类中心数 % theta_N : 每一聚类中心中最少的样本数，少于此数就不作为一个独立的聚类 % theta_S ：一个聚类中样本距离分布的标准差 % theta_c :

I am doing some PCA using sklearn.decomposition.PCA. I found that if the input matrix X is big, the results of two different PCA instances for PCA.transform will not be the same. For example, when X is a 100x200 matrix, there will not be a problem. When X is a 1000x200 or a 100x2000 matrix, the results of two different PCA instances will be different. I am not sure what's the cause for this: I suppose there is no random elements in sklearn's PCA solver? I am using sklearn version 0.18.1. with python 2.7 The script below illustrates the issue. import numpy as np import sklearn.linear_model as