These experts can recognize different moths, so they not only record the feature values, but also label that data with the actual moth species.

这些专家能够识别不同飞蛾， 因此他们不仅记录特征值，还会用实际飞蛾种类来标注这些数据。

And if for your question, it's necessary to really compile these traits values into categories, then you can do that.

而为了你问题， 确实需要将这些特征值归类，那么你可以这样做。

But when I read how they described it to each other, talking about eigenvalues and

但当我读到他们彼此描述时，讨论特征值及各种数学术语，这些容变得有意义了。

If you actually try computing the eigen values of a rotation like this, notice what happens.

你试着计算像这样旋转特征值 注意会发生什么。

And the way to interpret this is that all the basis vectors are eigenvectors, with the diagonal entries of this matrix being their eigen values.

解释这个方法是所有基向量都是特征向量 这个对角线元素是它们特征值。

So finding the eigen vectors and their eigen values of the matrix A comes down to finding the values of V and lambda that make this expression true.

所以求出A特征向量和它们特征值归结为求出V和使这个表达式成立值。

A is the matrix representing some transformation with V as the icon vector, and lambda is a number, namely the corresponding eigen value.

A是表示以V为图标向量某个变换 lambda是一个数字 即对应特征值。

The only eigen value is two, but every vector in the plane gets to be an eigenvector with that eigen value.

唯一特征值是2 但是平面上每个向量都是具有这个特征值特征向量。

In this case, by the way, the corresponding eigen value would have to be one, since rotations never stretcher squish anything, so the length of the vector would remain the same.

顺便说一下 在这种情况下 对应特征值必须是1 因为旋转不会拉伸压扁任何东西 所以向量长度保持不变。

For those of you wondering why we care about alternate coordinate systems, the next video on eigen vectors and eigen values, we'll give a really important example of this.

对于那些想知道为什么我们要关心交替坐标系人 下一集关于特征向量和特征值视频 我们会给出一个很重要例子。

Confusion about agon stuffs usually has more to do with the shaky foundation in one of these topics than it does with eigenvectors and eigen values themselves.

对正交困惑通常更多是与这些主题中一个不稳定基础有关 而不是与特征向量和特征值本身有关。

All of the vectors on the x axis are eigen vectors with eigen valued one, since they remain fixed in place.

x轴上所有向量都是特征值为1特征向量 因为它们保持固定。

Of course, there's nothing special about stretching versus squishing, or the fact that these eigen values happen to be positive.

当然 拉伸和挤压并没有什么特别 或者说这些特征值恰好是正。