pca smc

#include #include #include #include #pragma comment(lib,"cv200.lib")#pragma comment(lib,"highgui200.lib")#pragma comment(lib,"cxcore200.lib")double Coordinate[21]={ 1.5,2.3, 3.0,1.7, 1.2,2.9, 2.1,2.9, 3.1,2.6, 5.2,2.4, 6.1,8.0, 8.6,9.2, 1.0,2.0, 5.0,

InPCAIn other places, when solving extreme values, we always encounterX 'A' axFunction pairXI have always seen the results given by others. Today I have deduced the results carefully. Here I will record this process. The following is a proof of

of attributes, that is, the reduction of the dimension. Dimension reduction reduces the dimension of a dataset by creating new properties and merging some old properties together. Dimensional disasters: This is a phenomenon in which many data analysis becomes very difficult as data dimensions increase. Linear algebra techniques for dimensional regression: Principal component Analysis (PCA), singular value decomposition (SVD). 4. Feature subset Select

. %--------------------------------------------------------------------------%% calculate sample covariance r= CoV (z '); %1 means dividing by N to calculate covariance %% whitening Z [UNBSP;DNBSP;~]NBSP;=NBSP;SVD (r, ' econ '); % with EIG, [U,d]=eig (R); %% The following whitening matrix t=u*inv (sqrt (D)) *u ';% is called the inverse RMS of the covariance matrix, The INV calculation is not too time consuming because D is a diagonal array. Inv (sqrt (D)) *u ' is also a viable whitening matrix

"Reprint please indicate the source" Chenrudan.github.io Recently looked at some things about the dimensionality reduction algorithm, this article first gives out seven kinds of algorithms of an information table, this paper sums up the parameters of each algorithm, the main purpose of the algorithm and so on, and then introduces some basic concepts of dimensionality reduction, including what is the dimension of dimensionality reduction, why dimensionality reduction and dimensionality reduction

Document directory Comment 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: The last time I wrote about PCA and Lda, there are two ways to implement PCA: one is to implement feature value decomposition and the other

Advanced shellcode Programming Technology on windows Preface Concept of binary function shellcode 3. Selection of advanced languages Iv. x86 c2shellcode framework 5x64 c2shellcode framework Summary 7. Thank you Preface The technology described in this article has been used in practice for a long time, but it has not been documented for some reasons. This article provides code implementation for the key points and adds some new understandings from the author. The directory structure of the test c

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:Last time I wrote about PCA and LDA, there are two general implementations of PCA, one is realized by the decomposition of eigenvalue, and one is realized by singular va

Mathematics in Machine learning (5)-powerful matrix singular value decomposition (SVD) and its applicationCopyright 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:Last time I wrote about PCA and LDA, there are two general implementations of

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: The last time I wrote about PCA and LDA, there are two ways to implement PCA: one is to implement feature value decomposition and the other is to implement it using Singular

Principal component Analysis (principal component, or PCA) is a classic and simple machine learning algorithm whose main purpose is to use fewer variables to explain most of the variation in the original data. It is expected that many variables with high correlation can be converted into independent variables, and some new variables which are less than the number of original variables and which can explain most of the data variation are selected to ac

Brief history of self-modified code Self-modified code has a wide range of uses: 1. It was very difficult to use SMC (self-protection code) to protect applications 10 to 20 years ago, even if it was used to put compiled code into the memory. 2. the emergence of 95/NT In The Middle Of 1990s confused programmers about how to protect applications in the new operating system. I don't know how to port the protection measures to this new version. it is no l

tried it and the result is not correct. I found the source code of this tool. I used the source code for step-by-step debugging and tracked a branch. The author wrote a comment. This is the third-generation firmware encryption method, there is no decryption hope in the Update log. (Original article: Fixed crashes with 3. Generation-No decryption possible -) This is the end of things. It took two days last weekend to get the absolute coordinates of GPS... However, I am not worried about writ

of the T (TERM) Row. Each column is an article and each row is a word, the number of times that the current word of each cell appears in the current article. U is a matrix of R columns in T rows, V is a matrix of D columns in R rows, and s is the diagonal matrix of R columns in R rows. Here, the size of R is the rank of. In this case, U and V are the singular vectors of A, while S is the singular values of. The orthogonal unit feature vector of AA' is U, and the feature value is S. The orthogon

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 contactObjective:Last time I wrote about PCA and LDA, there are two general implementations of PCA, one is realized by the decomposition of eigenvalue, and one is realized by singular value decomposition.

The previous content of this article is "Factor Analysis". Due to its extraordinary theories, I plan to finish the entire course and then write it again. Before writing this article, I have read PCA, SVD, and lda. These models are similar, but they all have their own characteristics. This article will first introduce PCA. The relationship between them can only be learned and understood.

pca-albinism) assume that the data has been scaled to a reasonable interval. Example: When dealing with natural images, the pixel values we get are in [0,255] intervals, and the usual processing is to divide the pixel values by 255 to make them scale to [0,1]. Reduction of the mean value on a per-sample basis If your data is stationary (that is, the statistics for each dimension of the data are subject to the same distribution), you can consider subt

Question: In the document-term matrix created in IR, two word items are "Learn" and "study". In the traditional vector space model, the two are considered independent. However, from the semantic point of view, the two are similar, and their appearance frequency is similar. Can they be combined into a feature? The feature selection problem mentioned in Model Selection and normalization is that the features to be removed are mainly those irrelevant to class tags. For example, the "Student name" ha

Currently, dynamic routing and Static Routing are widely used. Here we mainly analyze the statements and steps of dynamic routing configuration for dynamic routing. Dynamic Routing means that the dynamic routing protocol (such as RIP) automatically creates a route table. When you remove a line, it automatically removes its route. Each interface in the dynamic routing configuration corresponds to a different network, and the IP addresses of the two endpoints connecting the two routers should belo

includethe following most excellent sites:stephen Becker ' s Page,raghunanda N H. Keshavan ' Spage,nuclear Norm and Matrix recoverythrough SDP bychristoph helmberg, Arvind Ganesh ' Slow-rank Matrix Recovery and completion via convex optimizationwho provide more in-depth additi Onal information.additional codes were featured also Onnuit Blanche. The following people provided additional Inputs:olivier Grisel,matthieu puigt. Most of the algorithms listed below generally rely on using the nuclear