Seven Techniques for Data Dimensionality Reduction Seven Techniques for Data Dimensionality Reduction 12 May, 2015 - 12:38 — rs The recent explosion of data set size, in number of records and attributes, has triggered the development of a number of b…
data compression可以使数据占用更少的空间,并且能使算法提速 什么是dimensionality reduction(维数约简) 例1:比如说我们有一些数据,它有很多很多的features,取其中的两个features,如上图所示,一个为物体的长度用cm来度量的,一个也是物体的长度是用inches来度量的,显然这两上features是相关的,画到上图中,近似于一条直线,之所以点不在一条直线上,是因为我们在对物体测量长度是会取整(对cm进行取整,对inches进行取整),这样的…
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…
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…
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…
博客内容取材于:http://www.cnblogs.com/tornadomeet/archive/2012/06/24/2560261.html Deep learning:三十五(用NN实现数据降维练习) Deep learning:三十四(用NN实现数据的降维) Deep learning:三十三(ICA模型) Deep learning:三十二(基础知识_3) Deep learning:三十一(数据预处理练习) Deep learning:三十(关于数据预处理的相关技巧) Deep…
