pca spss

Source: http://blog.csdn.net/xizhibei ================================== PCA, that is, principalcomponents analysis, is a very good algorithm, according to the book: Find the projection method that best represents the original data in the sense of least square. Then I said: it is mainly used for feature dimensionality reduction. In addition, this algorithm also has a classic application: face recognition. Here, we just need to take each line of the pr

Reference: Lle principle SummaryPersonal understandingthe defect of PCA dimensionality reduction : There are some linear relationships between samples in high dimensional space, and these relationships are not preserved after dimensionality reduction. For example, in a high-dimensional space, the shortest path is not the shortest line between two points in three-dimensional space, but the distance of the surface, and when we drop the dimension, it bec

PcaMaximize the sensitivity of individual dimensions and reduce the impact between dimensions. Differentiated measures: Variance dimension Effects: Covariance is thus represented by a matrix as the eigenvalues and eigenvectors of the covariance matrix. 1. Construct The matrix. 2. Find the covariance matrix of the Matrix. 3. Find the eigenvalues and eigenvectors of the covariance matrix, which are invariant in these directions. 4. Select a few of these directions to compare the full representatio

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

Softmax has been entangled for two days. The reason is that you accidentally changed the main program or pasted the code as usual. If you need it, you can go to the UFLDL tutorial. The effect is the same as that of UFLDL, I won't repeat the textures. ps: the code is matlab, not python's PCA and Whitening: pca_gen.m [python] % ======================================== ============================ x = sampleIMAGESRAW (); figure ('name', 'raw images'); ra

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

PCA (principal component analysis) is a common data dimensionality reduction method, which aims to reduce the computational amount by converting high-dimensional data to lower dimensions under the premise of less "information" loss. The "information" here refers to the useful information contained in the data.Main idea: From the original characteristics of a set of "importance" from the large to the small arrangement of new features, they are the orig

The source of this article: Http://blog.csdn.net/xizhibei=============================PCA, also known as principalcomponents analysis , is a very good algorithm, according to the book:Looking for the projection method that best represents the original data in the least mean square senseAnd then his own argument is: mainly used for features of the dimensionality reductionIn addition, the algorithm also has a classic application: human face recognition.

The source of this article: Http://blog.csdn.net/xizhibei=============================PCA, also known as principalcomponents analysis , is a very good algorithm, according to the book:Looking for the projection method that best represents the original data in the least mean square senseAnd then his own argument is: mainly used for features of the dimensionality reductionIn addition, the algorithm also has a classic application: human face recognition.

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

Python3 Learning API UsagePrincipal component analysis method for reducing dimensionUsing the data set on the network, I have downloaded to the local, can go to my git referenceGit:https://github.com/linyi0604/machinelearningCode:1 fromSklearn.svmImportlinearsvc2 fromSklearn.metricsImportClassification_report3 fromSklearn.decompositionImportPCA4 ImportPandas as PD5 ImportNumPy as NP6 " "7 principal component analysis:8 feature to reduce the dimensions of the method. 9 extracting major feature

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