Graph Regularized Feature Selection with Data Reconstruction

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Graph Regularized Feature Selection with Data Reconstruction

Abstract • 从图正则数据重构方面处理无监督特征选择: • 模型的思想是所选特征不仅通过图正则保留了原始数据的局部结构,也通过线性组合重构了每个数据点: • 所以重构误差成为判断所选特征质量的自然标准. • 通过最小化重构误差,选择最好保留相似性和判别信息的特征: 1 Introduction • 目前有两大类无监督特征选择算法:Similarity preserving 和 clustering performance maximization:Similarity preserv…

【转】[特征选择] An Introduction to Feature Selection 翻译

中文原文链接:http://www.cnblogs.com/AHappyCat/p/5318042.html 英文原文链接: An Introduction to Feature Selection 下面的中文译文侧重从原理上进行解释,但是在实际的应用中往往侧重的是实现过程, 可以看考这个链接,描述的比较详细,需要细细的学习:http://blog.csdn.net/bryan__/article/details/51607215 [中文原文] 你需要哪些特征来构建一个预测模型? 这是一个困难的…

单因素特征选择--Univariate Feature Selection

An example showing univariate feature selection. Noisy (non informative) features are added to the iris data and univariate feature selection(单因素特征选择) is applied. For each feature, we plot the p-values for the univariate feature selection and the cor…

Feature Selection Can Reduce Overfitting And RF Show Feature Importance

一.特征选择可以减少过拟合代码实例该实例来自机器学习实战第四章 #coding=utf-8 ''' We use KNN to show that feature selection maybe reduce overfitting ''' from sklearn.base import clone from itertools import combinations import numpy as np from sklearn.model_selection import train_t…

highly variable gene | 高变异基因的选择 | feature selection | 特征选择

在做单细胞的时候,有很多基因属于noise,就是变化没有规律,或者无显著变化的基因.在后续分析之前,我们需要把它们去掉. 以下是一种找出highly variable gene的方法: The feature selection procedure is based on the largest difference between the observed coefficient of variation (CV) and the predicted CV (estimated by a no…