[Scikit-learn] 2.5 Dimensionality reduction - Probabilistic PCA & Factor Analysis

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[Scikit-learn] 2.5 Dimensionality reduction - Probabilistic PCA & Factor Analysis

2.5.4. Factor Analysis PPCA的基本性质以及人肉推导: 以上假设z是标准正态分布的情况.以下是对z的分布的扩展,为general normal distribution. From: http://cs.brown.edu/courses/cs195-5/spring2012/lectures/2012-04-24_factorEM.pdf PPCA可以选择input data,保持不变性:FA不能这么搞. 但它俩都可以选择latent variables. From:…

机器学习课程-第8周-降维(Dimensionality Reduction)—主成分分析(PCA)

1. 动机一:数据压缩第二种类型的无监督学习问题,称为降维.有几个不同的的原因使你可能想要做降维.一是数据压缩,数据压缩不仅允许我们压缩数据,因而使用较少的计算机内存或磁盘空间,但它也让我们加快我们的学习算法. 但首先,让我们谈论降维是什么.作为一种生动的例子,我们收集的数据集,有许多,许多特征,我绘制两个在这里. 将数据从二维降一维: 将数据从三维降至二维: 这个例子中我们要将一个三维的特征向量降至一个二维的特征向量.过程是与上面类似的,我们将三维向量投射到一个二维的平面上,强迫使得所…

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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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many Machine Learning problems involve thousands or even millions of features for each training instance. not only does this make training extremely slow,it can also make it much harder to find a good solution. this problem is often referred to as th…