pca boxes

Traditional one-dimensional PCA and LDA methods are based on image vectors in image recognition. In these face recognition technologies, 2D face image matrices must be first converted to 1D image vectors, then perform PCA or LDA analysis. The disadvantage is obvious: 1. After being converted to one dimension, the dimension is too large and the calculation workload increases. II. The training of principa

Text boxes, radio buttons, check boxes, related actions Copy Code code as follows: var sex=$ ("Input[name= ' Sex ']:checked"). Val (); Gets the value of a set of radio selected items var item=$ ("#sel option:selected"). Text (); Gets the text of the Select selected item var option_num=$ (' #sel '). Val (); Get the Select item index $ ("#sel") [0].selectedindex = 1; The second element of the Se

EXERCISE:PCA and Whitening No. 0 Step: Data Preparation UFLDL The downloaded file contains the dataset Images_raw, which is a 512*512*10 matrix, which is 10 images of 512*512 (a) data-loading Using the Sampleimagesraw function, extract the numpatches image blocks from the Images_raw, each image block size is patchsize, and the extracted image blocks are stored in columns, respectively, in each column of the matrix patches, That is, patches (:, i) holds all the pixel values of the first image blo

This article mainly introduces four knowledge points, which is also the content of my lecture. 1.PCA Dimension reduction operation; PCA expansion pack of Sklearn in 2.Python; 3.Matplotlib subplot function to draw a child graph; 4. Through the Kmeans to the diabetes dataset clustering, and draw a child map. Previous recommendation:The Python data Mining course. Introduction to installing Python and crawler"

This article is mainly on the jquery to the radio box, multiple marquee, text boxes and other common operations are introduced, the need for friends can come to the reference, I hope to help you. A, text box, radio button, check box, related operations nbsp; nbsp; code are as follows: Var sex=$ ("Input[name= ' Sex ']:checked"). Val (); nbsp;//Get a set of radio selected values nbsp; var item=$ ("#sel option:selected"). Text (); nbsp; nbsp; nbsp;//Ge

[JavaScript] select all or invert check boxes, determine which check boxes are selected, and select all javascript check boxes. This function is not difficult to select and select all check boxes and determine which check boxes are selected, The use of document. getElementsB

[JQuery] Select All and invert check boxes to determine which check boxes are selected. jquery Selects all check boxes. This article selects and deselected all the check boxes in [JavaScript] to determine which check boxes are selected (click to open the link) as a companion

The re-sequencing is cheap, and the sequencing and analysis of the population is also growing. The analysis of group structure is the most common analysis content of re-sequencing. The application of group structure analysis is very extensive, first of all, it is the most basic analysis content in the analysis of group evolution, secondly, when conducting GWAS analysis, it is necessary to use the results of PCA or structure analysis as a co-variable t

Four machine learning dimensionality reduction algorithms: PCA, LDA, LLE, Laplacian eigenmapsIn the field of machine learning, the so-called dimensionality reduction refers to the mapping of data points in the original high-dimensional space to the low-dimensional space. The essence of dimensionality is to learn a mapping function f:x->y, where x is the expression of the original data point, which is currently used at most in vector representations. Y

machine learning algorithm -PCA dimensionality reduction OneIntroductionThe problems we encounter in the actual data analysis problem usually have the characteristics of higher dimensionality, when we carry out the actual data analysis, we will not use all the features for the training of the algorithm, but rather pick out the features that we think may affect the target. For example, in the Titanic Crew survival prediction problem, we will use the na

EXERCISE:PCA and WhiteningNo. 0 Step: Data preparationUFLDL The downloaded file contains the dataset Images_raw, which is a 512*512*10 matrix, which is 10 images of 512*512(a) data-loadingUsing the Sampleimagesraw function, extract the numpatches image blocks from the Images_raw, each image block size is patchsize, and the extracted image blocks are stored in columns, respectively, in each column of the matrix patches, That is, patches (:, i) holds all the pixel values of the first image block(b

Later, sift had two extensions that used the PCA concept. 1. PCA-SIFT The PCA-SIFT has the same sub-pixel location (sub-pixel), scale, and dominant orientations as the standard sift, but when the description is calculated in step 1, it uses 41 × 41 image spots around the feature points to calculate its principal component, and uses a

, reduce the accuracy, and through dimensionality reduction, we want to reduce the error caused by redundant information, improve the accuracy of identification (or other applications). Or we hope to find the intrinsic structural features of the data through the dimensionality reduction algorithm.In many algorithms, the reduced-dimension algorithm becomes a part of data preprocessing, such as PCA. In fact, there are some algorithms without dimensional

, contains redundant information and noise information, in the actual application of example recognition caused by error, reduce the accuracy, and through dimensionality reduction, we want to reduce the error caused by redundant information, improve the accuracy of identification (or other applications). Or we hope to find the intrinsic structural features of the data through the dimensionality reduction algorithm.In many algorithms, the reduced-dimension algorithm becomes a part of data preproc

What PCA needs to do is to de-noising and de-redundancy, the essence of which is the diagonalization covariance matrix.I. Pre-knowledge1.1 Covariance analysisFor the general distribution, the direct generation of E (X) and the like can be calculated, but really give you a specific numerical distribution, to calculate the covariance matrix, according to the formula to calculate, it is not easy to react. There is not much information on the Internet, he

Copyright: This article by leftnoteasy released in http://leftnoteasy.cnblogs.com, this article can be all reproduced or part of the use, but please note the source, if there is a problem, please contact the wheeleast@gmail.com Preface: Article 2ArticleHe gave me a lot of machine learning suggestions when he went out outing with the department boss, which involved a lotAlgorithmAnd learning methods. Yi Ning told me last time that if we learn classification algorithms, we 'd better start wi

IntroductionPrincipal component Analysis (PCA) is a data dimensionality reduction algorithm which can greatly improve the learning speed of unsupervised features. More importantly, the understanding of PCA algorithm, the implementation of the whitening algorithm has a great help, many algorithms are first used whitening algorithm for preprocessing steps.Suppose you use an image to train the algorithm, becau

theoretical knowledge of K-L transformationK-L transformation is another common feature extraction method besides PCA, it has many forms, the most basic form is similar to PCA, it differs from PCA in that PCA is a unsupervised feature transformation, and K-L transform can take different classification information and r

Mathematics in Machine learning (4)-Linear discriminant analysis (LDA), principal component analysis (PCA)Copyright Notice:This article is published by Leftnoteasy in Http://leftnoteasy.cnblogs.com, this article can be reproduced or part of the use, but please indicate the source, if there is a problem, please contact [email protected]Objective:The second article talked about, and department Kroning out outing, he gave me quite a lot of machine learni

The main content of this article is from Andrew's book, linked to http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening/ PCA PCA, also known as principal component analysis, is a means of dimensionality reduction, which can significantly improve the speed of the algorithm.When you are working with an image, the input is usually redundant because the adjacent pixels in the image are often associated,