pca

Sklearn PCA is pca.components_ the loadings? I am pretty sure it is, but I am trying to follow along a research paper and I am getting different results from their loadings. I can't find it within the sklearn documentation. pca.components_ is the orthogonal basis of the space your projecting the data into. It has shape (n_components, n_features) . If you want to keep the only the first 3 components (for instance to do a 3D scatter plot) of a datasets with 100 samples and 50 dimensions (also named features), pca.components_ will have shape (3, 50) . I think what you call the "loadings" is the

While using princomp() function in R, the following error is encountered : "covariance matrix is not non-negative definite" . I think, this is due to some values being zero (actually close to zero, but becomes zero during rounding) in the covariance matrix. Is there a work around to proceed with PCA when covariance matrix contains zeros ? [FYI : obtaining the covariance matrix is an intermediate step within the princomp() call. Data file to reproduce this error can be downloaded from here - http://tinyurl.com/6rtxrc3] The first strategy might be to decrease the tolerance argument. Looks to me

My code: from numpy import * def pca(orig_data): data = array(orig_data) data = (data - data.mean(axis=0)) / data.std(axis=0) u, s, v = linalg.svd(data) print s #should be s**2 instead! print v def load_iris(path): lines = [] with open(path) as input_file: lines = input_file.readlines() data = [] for line in lines: cur_line = line.rstrip().split(',') cur_line = cur_line[:-1] cur_line = [float(elem) for elem in cur_line] data.append(array(cur_line)) return array(data) if __name__ == '__main__': data = load_iris('iris.data') pca(data) The iris dataset: http://archive.ics.uci.edu/ml/machine

PCA ：主成分分析 相关矩阵，找特征值，找每个特征值对应特征向量，即组成主组成式子： 每个式子指向一个结果 y ，找一条线将这些 y 分开。有 11 个变量就有 11 个新坐标轴，通过点到直线距离来区分。 信息必须集中在前几个主成分上。比如 PC1 表示 3 个变异。 主成分分析的前提是原始数据不能不同 x 指向同一个 y 。 主成分分析不能用来代表某一组因素的共同作用。 PCA 是一类因子分析，在特征值提取时可以选择不同算法。 取第一列和第二列主成分，可以得到二维图： 通过改变坐标轴可将差异表达的更清楚。 PCA 和 cluster 区别是 Cluster 目标是将 y 分类， PCA 将特征值分类。 对应分析：卡方分析反映出期望与观测值的差异，即是信息点，行列之间的不规律性。对卡方矩阵做主成分分析，原矩阵和转置后的矩阵都做一遍。所以 PCA 与 CA 的比较： CA 要求原始数据可以不单调，不要求正态。 PCA 要求原始数据可以不单调，最后的主成分转化为一个个欧式距离，要求正态。 来源： https://www.cnblogs.com/yuanjingnan/p/11795984.html

I'm trying a classification with python. I'm using Naive Bayes MultinomialNB classifier for the web pages (Retrieving data form web to text , later I classify this text: web classification). Now, I'm trying to apply PCA on this data, but python is giving some errors. My code for classification with Naive Bayes : from sklearn import PCA from sklearn import RandomizedPCA from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB vectorizer = CountVectorizer() classifer = MultinomialNB(alpha=.01) x_train = vectorizer.fit_transform(temizdata)

Im reading Abdi & Williams (2010) "Principal Component Analysis", and I'm trying to redo the SVD to attain values for further PCA. The article states that following SVD: X = P D Q^t I load my data in a np.array X. X = np.array(data) P, D, Q = np.linalg.svd(X, full_matrices=False) D = np.diag(D) But i do not get the above equality when checking with X_a = np.dot(np.dot(P, D), Q.T) X_a and X are the same dimensions, but the values are not the same. Am I missing something, or is the functionality of the np.linalg.svd function not compatible somehow with the equation in the paper? TL;DR: numpy's

I'm trying to build a predictive model in caret using PCA as pre-processing. The pre-processing would be as follows: preProc <- preProcess(IL_train[,-1], method="pca", thresh = 0.8) Is it possible to pass the thresh argument directly to caret's train() function? I've tried the following, but it doesn't work: modelFit_pp <- train(IL_train$diagnosis ~ . , preProcess="pca", thresh= 0.8, method="glm", data=IL_train) If not, how can I pass the separate preProc results to the train() function? As per the documentation, you specify additional preprocessing arguments with trainControl ?trainControl ..