pca python

Principle analysis of PCA algorithm for principal component analysesDiscussion on the understanding of principal component Analysis (PCA) algorithmPrincipal component Analysis (PCA): dimensionality reduction . Multiple variables are selected by linear transformation (linear addition) to select fewer important variables. The principle of minimizing the loss

Currently, the PCA algorithm is widely used in image processing. When the feature dimension of the extracted image is relatively high, in order to simplify the calculation and storage space, the high-dimensional data needs to be reduced to a certain extent, and the data is not distorted as much as possible. Let's give an example to make it easy to understand: 1) for a training set, 100 samples (I =, 3 ,..., 100), feature Xi is 20 dimensions. [xi1, xi

This series is a personal learning note for Andrew Ng Machine Learning course for Coursera website (for reference only)Course URL: https://www.coursera.org/learn/machine-learning Exercise 7--k-means and PCA Download coursera-Wunda-Machine learning-all programming practice answers In this exercise, you will implement the K-means clustering algorithm and apply it to compressed images. In the second section, you will use principal component analysis to f

PCA principal component Analysis method, LDA linear discriminant analysis method, can be considered as supervised data dimensionality reduction. The following code implements two ways to reduce the dimension, respectively:Print(__doc__)ImportMatplotlib.pyplot as Plt fromSklearnImportDatasets fromSklearn.decompositionImportPCA fromSklearn.discriminant_analysisImportLineardiscriminantanalysisiris=Datasets.load_iris () X=Iris.datay=Iris.targettarget_name

PCA, principal component analysis　　Principal component analysis is mainly used for dimensionality reduction of data. The dimensions of the data features in the raw data may be many, but these characteristics are not necessarily important, and if we can streamline the data features, we can reduce the storage space and possibly reduce the noise interference in the data.For example: Here is a set of data, as shown in table 1 below2.5 1.2-2.3-2.8-1 0.33.3

Many times there will be a n*m matrix as a PCA (M-dimensionality) and then get a m* (M-1) matrix such a result. Before it was mathematically deduced to get this conclusion, however,See a very vivid explanation today:Consider what PCA does. Put simply, PCA (as most typically run) creates a new coordinate system by (1) shifting the origin to the centroid of your Da

Transferred from: http://blog.csdn.net/kklots/article/details/8247738 Recently, as a result of the curriculum needs, has been studying through the face to judge gender, in the OPENCV contrib provides two methods that can be used to identify gender: Eigenface and Fisherface,eigenface mainly using PCA (principal component analysis), By eliminating the correlation in the data, the high-dimensional image is reduced to the low-dimensional space, the sample

Why the ICA on UFLDL must do PCA whitenMr. Andrew Ng's UFLDL tutorial is a preferred course for deep learning beginners. Two years ago, when I looked at the ICA section of the tutorial, I mentioned that when using the ICA model described in the tutorial, the input data had to be PCA-whitening, and a todo on the page asked why. My understanding of machine learning at that time did not answer this question, j

information are contained, which creates errors in the actual application sample image recognition, reducing the accuracy.,We hope to reduce the error caused by redundant information.,Improves the accuracy of recognition (or other applications. (2) You may want to use a dimensionality reduction algorithm to find the essential structural features inside the data. (3) Use dimensionality reduction to accelerate subsequent computing (4) There are many other purposes, such as solving the sparse

Using PCA to reduce the dimension of high-dimensional data, there are a few features:(1) data from high-dimensional to low-dimensional, because of the variance, similar features will be merged, so the data will be reduced, the number of features will be reduced, which helps to prevent the occurrence of overfitting phenomenon. But PCA is not a good way to prevent overfitting, it is better to regularization t

In fact, should be the first to tidy up the PCA, Zennai has no time, may be their own time is not sure, OK, below into the topic. the concept of dimensionality reductionThe so-called dimensionality reduction is to reduce the dimensionality of the data. In machine learning is particularly common, before doing a picture to extract the wavelet feature, for a picture of a size of 800*600, if each point to extract five scale, eight directions of the

PCA real operation in the big pit really is not hurt ah .... Today, we are talking about a problem with a subconscious error. In my blog There are two other articles reproduced in the blog is a record of the idea of PCA, there is a need to see. Mat m (Ten, 2, cv_32f, Scalar (0)); Mat dt = cv::mat_ The principal component characteristics obtained are: As can be seen from the above, two principal comp

Robust PCARachel Zhang 1. RPCA Brief Introduction1. Why use Robust PCA? Solve the problem witheat Ike noise with high magn1_instead of Gaussian distributed noise. 2. main ProblemGiven C = A * + B *, where A * is a sparse spike noise matrix and B * is a Low-rank matrix, aiming at recoveringB *. B * = U Σ V ', in which U ε Rn * k, Σ ε Rk * k, V ε Rn * k 3. difference from pcabth PCA and Robust PCAaims at Matr

Given n m -dimensional samples x (1), x(2),...,x(n), suppose our goal is to reduce these n samples from m -dimensional to k -dimensional, and as far as possible to ensure that the operation of this dimension does not incur significant costs (loss of important information). In other words, we want to project n sample points from m -dimensional space to K -dimensional space. For each sample point, we can use the following formula to represent this projection process: Z=ATX (1) where x is the M-dim

PrefaceThis section is mainly to practice the use of PCA,PCA whitening and Zca whitening on 2D data, 2D data set is 45 data points, each data point is 2 dimensions.Some MATLAB functionsColor Scatter point graph function: Scatter (x,y,c,s) x, y is two vectors for locating data points, S is the size of the plot point, C is the color used for the drawing, S and C can be given as vectors or expressions, s and C

Principal Component Analysis Algorithm advantages and disadvantages: Pros: Reduce data complexity and identify the most important features Cons: Not necessarily required, and may lose useful information Applicable data type: numeric data Algorithmic thinking: The benefits of dimensionality reduction: Make data sets easier to use Reduce the computational overhead of many algorithms Noise removal Make the results understandable The idea of principa

Simple principal component analysis. The first time I saw PCA, my understanding was to try to describe the data in less dimensions to achieve the desired (though not the best, but ' cost-effective ' highest) effect.clear;% parameter initialization inputfile = ' F:\Techonolgoy\MATLAB\file\MTALAB data analysis and Mining \datasets\chapter4\chapter4\ sample program \ Data\principal_component.xls '; outputfile = ' F:\Techonolgoy\MATLAB\file\MTALAB data an

Data = read. Table ("file", header = true) R commands for PCA Here are some r commands for PCA Pcdat = princomp (data)-It does actual job and put the results to pcdat. It will use Covariance Matrix Pcdat = princomp (data, Cor = true)-it will use correlation matrix Summary (pcdat)-It will print standard deviation and proportion of variances for each component Screeplot (pcdat)-It will plot screeplt Bi

Http://blog.csdn.net/ijuliet/archive/2009/10/07/4640624.aspx Scale-invariant feature transform (SIFT), Lowe, 2004 PCA-SIFT (Principle Component Analysis), Y. Ke, 2004 Surf, Bay, 2006 The three teams have their own merits. They are the three sisters of Song in the field of Image Feature Detection! The PCA-SIFT used the histogram method in sift for the primary meta-analysis method. The two magic weapons of

related to the class label, but there is noise or redundancy. In this case, a feature dimensionality reduction method is needed to reduce the number of features, reduce noise and redundancy, and reduce the likelihood of excessive fitting. A method called Principal component Analysis (PCA) is discussed below to solve some of the above problems. The idea of PCA is to map n-dimensional features onto D-dimensi