pca smc

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,

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

1.PCA Algorithm Overview introduction of 1.1 PCA algorithm PCA (Principal Component analysis) is a statistical process that converts a set of observation values of a possible correlation variable into a set of linearly independent variable values by means of an orthogonal transformation, known as the principal component. The number of principal components is le

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

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

Principal component Analysis (PCA) is an effective method of compressing and de-noising the data based on the covariance matrix of variables, the idea of PCA is to map n-dimensional features to K-Dimension (KRelated knowledgeIntroduction to a PCA Tutorial: A tutorial on Principal components Analysis--lindsay I Smith1. Covariance covarianceThe covariance formula f

Principal Component Analysis (PCA) is a multivariate statistical analysis method that uses linear transformation to select a small number of important variables. It is also called Main Component analysis. In practice, many variables (or factors) related to this issue are often proposed for comprehensive analysis, because each variable reflects certain information of this topic to varying degrees. Principal component analysis is first introduced by K.

, 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

Http://matlabdatamining.blogspot.com/2010/02/principal-components-analysis.htmlEnglish principal components Analysis of the blog, write very good, worried after not open, full text reproduced.Principal Components AnalysisIntroductionReal-world data sets usually exhibit relationships among their variables. These relationships is often linear, or at least approximately so, making them amenable to common analysis techniques. One such technique isprincipal Component analysis("

This article is based on two references of the same name.A Tutorial on Principal Component Analysis. PCA, or principal component analysis, is mainly used for dimensionality reduction of features. If the number of features in the data is very large, we can think that only a part of the features are truly interesting and meaningful, while other features or noise, or redundant with other features. The process of finding meaningful features from all featu

Source: http://blog.csdn.net/zhongkelee/article/details/44064401 Reprint please declare the source: http://blog.csdn.net/zhongkelee/article/details/44064401 A brief introduction of PCA 1. Related background After Chenhonghong teacher's "machine learning and Knowledge discovery" and Tihaibo Teacher's "matrix algebra" two courses, quite experience. Recently in the master component analysis and singular value decomposition of the project, so record the

PCA (Principal Component analysis) is also known as the Karhunin-low transformation (Karhunen-loeve Transform), a technique used to explore high-dimensional data structures. PCA is often used for exploration and visualization of high-dimensional datasets. can also be used for data compression, data preprocessing and so on. PCA can synthesize high-dimensional vari

needs to be represented as a vector form to be trained in the input model. However, it is well known that the processing and analysis of high-dimensional vectors can greatly consume system resources and even create dimensional disasters. For example, in the field of CV (computer vision) to extract a 100x100 RGB image pixel features, the dimension will reach 30000, in NLP (Natural language Processing) in the field of The common dimensionality reduction methods include principal component analysi

1. Principle of PCA The main component analysis is often used in the field of image processing, and the advantage is that the dimension of the data to be analyzed is reduced, but the main information of the data can be retained. It works like this, for a given set of data (column vectors): When it is centered, it is represented as: where u is the mean value of the input column vector. The central data is U1 in the first spindle (both the main di

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