减少数据集的维数后，我得到了负特征值 [英] After reducing the dimensionality of a dataset, I am getting negative feature values

问题描述

我使用了降维方法(此处讨论:随机投影算法伪代码 )上的大型数据集.

I used a Dimensionality Reduction method (discussion here: Random projection algorithm pseudo code) on a large dataset.

将维度从1000减少到50后，我得到了新的数据集，每个样本如下所示:

After reducing the dimension from 1000 to 50, I get my new dataset where each sample looks like:

[1751.-360. -2069. ...，2694.-3295. -1764.]

[ 1751. -360. -2069. ..., 2694. -3295. -1764.]

现在我有点困惑，因为我不知道负特征值应该代表什么.可以具有这样的负面特征吗?因为在减少之前，每个样本都是这样的:

Now I am a bit confused, because I don't know what negative feature values supposed to mean. Is it okay to have negative features like this? Because before the reduction, each sample was like this:

3，18，18，18，126 ...

3, 18, 18, 18, 126 ...

这是正常现象还是我做错了什么?

Is it normal or am I doing something wrong?

推荐答案

我猜您从本文.

由于投影矩阵具有一些负数条目，所以可以将正投影映射到负值.因此，符号的变化并不表示错误.

As the projection matrix has some negative entries it is ok that the projection maps positve to negative values. So the change in the signs does not indicate an error.

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