[Scikit-learn] 1.2 Dimensionality reduction - Linear and Quadratic Discriminant Analysis

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[Scikit-learn] 1.2 Dimensionality reduction - Linear and Quadratic Discriminant Analysis

Ref: http://scikit-learn.org/stable/modules/lda_qda.html Ref: http://bluewhale.cc/2016-04-10/linear-discriminant-analysis.html Ref: http://blog.csdn.net/lizhe_dashuju/article/details/50329663 [该系列,作者很用心,讲得很通透] 线性判别分析(Linear Discriminant Analysis)简称LD…

[Scikit-learn] 4.4 Dimensionality reduction - PCA

2.5. Decomposing signals in components (matrix factorization problems) 2.5.1. Principal component analysis (PCA) 4.4. Unsupervised dimensionality reduction 4.4.1. PCA: principal component analysis PCA+ICA 解混过程:https://www.zhihu.com/question/28845451…

论文解读（LLE）《Nonlinear Dimensionality Reduction by Locally Linear Embedding》and LLE

论文题目:<Nonlinear Dimensionality Reduction by Locally Linear Embedding > 发表时间:Science 2000 论文地址:Download 简介局部线性嵌入(Locally Linear Embedding,简称LLE)重要的降维方法. 传统的 PCA,LDA 等方法是关注样本方差的降维方法,LLE 关注于降维时保持样本局部的线性特征,由于LLE在降维时保持了样本的局部特征,所以广泛用于图像图像识别,高维数据可视化等领域.…

[UFLDL] Dimensionality Reduction

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可视化MNIST之降维探索Visualizing MNIST: An Exploration of Dimensionality Reduction

At some fundamental level, no one understands machine learning. It isn’t a matter of things being too complicated. Almost everything we do is fundamentally very simple. Unfortunately, an innate human handicap interferes with us understanding these si…

(原创)（三）机器学习笔记之Scikit Learn的线性回归模型初探

一.Scikit Learn中使用estimator三部曲 1. 构造estimator 2. 训练模型:fit 3. 利用模型进行预测:predict 二.模型评价模型训练好后,度量模型拟合效果的常见准则有: 1. 均方误差(mean squared error,MSE): 2. 平均绝对误差(mean absolute error,MAE) 3. R2 score:scikit learn线性回归模型的缺省评价准则,既考虑了预测值与真值之间的差异,也考虑了问题…

第八章——降维（Dimensionality Reduction）

机器学习问题可能包含成百上千的特征.特征数量过多,不仅使得训练很耗时,而且难以找到解决方案.这一问题被称为维数灾难(curse of dimensionality).为简化问题,加速训练,就需要降维了. 降维会丢失一些信息(比如将图片压缩成jpeg格式会降低质量),所以尽管会提速,但可能使模型稍微变差.因此首先要使用原始数据进行训练.如果速度实在太慢,再考虑降维. 8.1 维数灾难(The Curse of Dimensionality) 我们生活在三维空间,连四维空间都无法直观理解,更别说更高…

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（原创）Stanford Machine Learning (by Andrew NG) --- (week 8) Clustering & Dimensionality Reduction

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