pca boxes

1. Background PCA (Principal Component analysis), the role of PAC is mainly to reduce the dimensions of the data set, and then select the basic features. The main idea of PCA is to move the axes and find the eigenvalues in the direction of the most variance. What is the eigenvalue of the direction with the greatest variance? Just like in the curve B. The same. It covers the widest range.Basic steps: (1) fir

PCA is an unsupervised learning algorithm, which can effectively reduce the latitude of data in the case of preserving most useful information.It is mainly used in the following three areas:1. Increase the speed of the algorithm2. Compress the data to reduce the consumption of memory and hard disk space3. Visualize data to map high latitude data to 2-D or 3-DAll in all, the PCA thing is to complete a mappin

1 Principal component Analysis(Principal Component ANALYSIS,PCA)2 linear discriminant Analysis(Linear discriminant analysis, LDA)Research backgroundIntroduction to basic knowledgeIntroduction to the Classic methodSummary discussionThe question is raisedGeographic systems are complex systems with multiple elements. In the study of geography, multivariate problems are often encountered. Too many variables will undoubtedly increase the difficulty and com

I. INTRODUCTION of PCA 1. Related background Principal component Analysis (Principal Component ANALYSIS,PCA) is a statistical method. An orthogonal transformation transforms a set of variables that may be related to a set of linearly unrelated variables, and the transformed set of variables is called the principal component. After finishing Chenhonghong teacher's "machine learning and Knowledge discovery" a

principal component Analysis ( Principal Component Analysis , PCA is a multivariate statistical analysis method that transforms multiple variables through a linear transformation to select fewer important variables. principle: When we use the statistical analysis method to study the multi-variable problem, the number of variables will increase the complexity of the subject. It is natural to expect a smaller number of variables to get more information.

This paper realizes PCA principal component analysis based on SVD singular matrix decomposition, uses this algorithm to complete the recognition of human face image, mainly explains the principle of SVD to realize PCA, how to use SVD to realize the dimensionality reduction of image features, and the application of SVD in text clustering, such as weakening synonyms, polysemy, and so on. Solve the problem tha

input_data = rand (1000,3); % randomly generated 1000 samples, each with x, Y, z three properties Figure (1);% control the drawing window to 1Hold off;% so that the current axis and graphics no longer have the nature of being refreshed, close on this basis and then drawPLOT3 (Input_data (:, 1), Input_data (:, 2), Input_data (:, 3), ' Ro '); Percent Function PCA, Input_data, Out_dim % use this to switch methodsuse_svd_method=1;% changed to 0 after u

http://blog.csdn.net/jerr__y/article/details/53188573This article mainly refer to the following article, the text of the code is basically the second article of the Code handwritten implementation of a bit.-PCA Explanation: http://www.cnblogs.com/jerrylead/archive/2011/04/18/2020209.html-Python implementation: http://blog.csdn.net/u012162613/article/details/42177327Overall code"" "The Total code. Func: The original characteristic matrix is reduced to

Http://blog.json.tw/using-matlab-implementing-pca-dimension-reductionWith M-pen information, each piece of material is n-dimensional, so that they can be regarded as a mxn matrix. If the size of the material is too large, it may be detrimental to analysis, such as this m-pen information used as a machine learning.The idea of PCA is to work out the oblique variance matrices of this MXN matrix, which is NXN,

In fact, previously written in the PCA-related blog post, but because the theoretical knowledge is limited, so the understanding is relatively shallow. This blog post, we understand the PCA in a different way, I assume that we have a preliminary understanding of PCA. First, let's cite an example in a two-dimensional space, such as: The left image represents five

1. PCA Holistic ThinkingPca,principle componet analysis, PCA, mainly used for data dimensionality reduction. By calculating the eigenvalues and eigenvectors of the covariance matrix of a given data set, it obtains the most critical direction of the dataset (the projection variance of the dataset is the largest in this direction, which can keep the most information), and the first k-dimensional space is sele

Welcome reprint, please indicate the source, I have limited ability, mistakes are unavoidable, welcome to guideBased on the first two posts, a sex classifier has been trained. Then this classifier should be used for gender classification.This test process is the same as the training process. However, during training, the sample data is processed in large quantities. The test process is processed for images that need to be identified. First, face recognition, that is, in the image to fin

Netease Open Course: 14th coursesNotes, 10 In the factor analysis mentioned earlier, the EM algorithm is used to find potential factor variables for dimensionality reduction. This article introduces another dimension reduction method, principal components analysis (PCA), which is more direct than factor analysis and easier to calculate. Principal component analysis is based on, In reality, for high-dimensional data, many dimensions are Disturb

principal Component Analysis 1. Description of the problem In many fields of research and application, it is often necessary to make a large amount of observations on multiple variables that reflect things, and collect large amounts of data for analysis to find the law. Large-scale multivariate samples will undoubtedly provide rich information for research and application, but also to some extent increase the workload of data collection, more importantly, in most cases, there may be correlations

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 is a black box type of dimensionality reduction, through mapping, hope that the projected data as far as possible, so to ensure that the map after the variance as large as possible, the direction of the next map and the current mapping direction orthogonalSteps of PCA:The first step: first to the current data (de-mean) to find the covariance matrix, covariance matrix = data * Data of the transpose/(M-1) m for the number of columns, the diagonal is

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