最先进的维度算法
我们知道有一些算法可以减少数据集的维度，例如 PCA 和 Isomap，
- 目前最先进的算法是什么？ 降低数据集的维度。
- 你有一个例子吗，也许是在 MATLAB 上？
假设我们有一个包含 100,000 个属性的数据集，例如 Dorothea 数据集 （由结构分子特征表示的化学化合物必须分为活性（与凝血酶结合）或非活性。这是 NIPS 2003 特征选择挑战赛的 5 个数据集之一。）
Data Set Characteristics: Multivariate
Number of Instances: 1950
Area: Life
Attribute Characteristics: Integer
Number of Attributes: 100000
Date Donated 2008-02-29
Associated Tasks: Classification
Missing Values? N/A
Number of Web Hits: 17103
We know there are algorithms to reduce the dimension of data sets like PCA and Isomap
- What is the state of the art in the
reducing dimensionality to data sets. - Do you have an example, maybe on MATLAB?
Lets say we have a data set with 100,000 attributes like Dorothea Data Set
(Chemical compounds represented by structural molecular features must be classified as active (binding to thrombin) or inactive. This is one of 5 datasets of the NIPS 2003 feature selection challenge.)
Data Set Characteristics: Multivariate
Number of Instances: 1950
Area: Life
Attribute Characteristics: Integer
Number of Attributes: 100000
Date Donated 2008-02-29
Associated Tasks: Classification
Missing Values? N/A
Number of Web Hits: 17103
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具体到Matlab，您可以从他们的统计工具箱。
查找特征选择和特征转换部分。另外，我会尝试 SVD、FastMap 和 RobustMap。您需要阅读一些相关内容，然后决定哪一个最适合您的数据。
Specific to Matlab, you can take some ideas from the manual of their Statistics Toolbox.
Look for the Feature Selection and Feature Transformation sections. Also, I would try SVD, FastMap and RobustMap. You'll need to read a bit about each and decide which one is most suitable for your data.
最大方差展开是当今特别流行的技术。一种称为“结构保留嵌入”的类似方法在 ICML 2009 上获得了最佳论文。其他一些技术包括拉普拉斯特征图、局部线性嵌入和核 PCA。
Maximum Variance Unfolding is a particularly popular technique these days. A similar approach called Structure Preserving Embedding got best paper at ICML 2009. A few other techniques include Laplacian Eigenmaps, Locally Linear Embedding, and Kernel PCA.