Overview PCA is a classical dimensionality reduction method. The basic PCA is a linear dimensionality reduction method and is essentially a linear coordinate transformation. There are two kinds of classical PCA objective optimization interpretation ideas: one is to maximize variance in a single dimension, and the other is to minimize the distance from the sample point to the hyperplane by using the idea of least square linear regression.

Reference Source: http://www.cnblogs.com/Hand-Head/articles/5156435.html http://www.matlabsky.com/thread-11751-1-1.html matlab Help Documents Program Source Code Download Link: http://download.csdn.net/detail/ckzhb/9903051 contains three M files: drtool_pca: function encapsulation.Box drawings are not included.Pca _ test: example of MATLAB-PCA () function princmp _ test: example of MATLAB-princmp () function example shows: It uses 9 indicators to measure the quality of life in 329 cities in the United States: climate, housing, health, crime rate, transportation, education, art, entertainment and economy. For

Brief Introduction to Emotional Analysis; Emotional analysis is to analyze whether a sentence is subjective or objective, and whether it expresses positive or negative emotions.Principles such as the following sentence: " The picture of this mobile phone is excellent and its operation is relatively smooth.But taking pictures is really terrible!The system is not good either. "(1) emotional words to analyze a sentence is positive or negative, the simplest and most

Information Quantity of Random Events In order to describe the information content of a random event, self-information is defined.
Self-information indicates the degree of uncertainty.
There is no information when an event is certain to occur.The smaller the probability of an event, the greater the amount of information.
The uncertainty brought by the unknown is the target to be described by the self-information.
Self-information: i (x) = log _ i \ frac { 1 } { p (x) } notice: the amount of self-information here does not represent the size of the role of information.

Several Scenarios of Bean Instantiation 1. instantiation of Beandefinitionregistrypostprocessor: extension of standard BeanFactoryPostProcessor. BeanFactoryPostProcessor is used to further define the registered BeanDefinition. IoC container is essentially bean management.Therefore, BeanActionTypostProcessor itself is also a Bean. To further define BeanActionTypostProcessor's BeanDefinition, register through BeanDefinitionRegistryPostProcessor. BeanDefinitionRegistryPostProcessor and its subclasses are the most instantiated Beans of Ioc container.They implement class call refresh () method call invokebeanfactorypostprocessors (beanfactory) in configurableapplicationcontext (applicationcontext subinterface);Method is instantiated when

Today the landlord continues to share a classic Java quiz:
Tell me about the concepts of AOP and IOC in spring and how they are applied in Spring.
AOP: Aspect Oriented Programming
IOC: Dependence Inversion or Inversion of Control
For the specific meaning of AOP and IOC in spring, the landlord has consulted relevant materials online and now shares them with you:
1. IOC(Inverse of Control): Inversion of Control, also known as dependency inversion.

Problem Description Recently, we will develop an article's region classifier for localization recommendation.In short, given an article, judge which of the following regions the article belongs to (or does not belong to any region): Dalian, Fuzhou, Chengdu, Chongqing, dongguan, Foshan, Nanjing, Harbin, Hangzhou, Jinan, Qingdao, Xiamen, Shenzhen, Shenyang, Suzhou, Tianjin, Wenzhou, Wuhan, Xi' an, Zhengzhou First google, avoid building wheels repeatedly, but did not find the relevant open source tools