Just like a few examples where this is used is fraud detection in banks, like some payment events go in, you aggregate feature vectors, some of that through a fraud model.

就像一些使用这个例子， 在银行中进行欺诈检测，比如有些支付事件进来， 你聚合向，其中一部分通过欺诈模型。

A sheer E.G. doesn't have enough eigen vectors to span the full space.

纯粹E G 没有足够向来张成整个空间。

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.

解释这个方法所有向都向 这个矩阵对角线元素它们值。

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.

所以求出矩阵A向和它们值归结为求出V和使这个表达式成立值。

It's a nightmare. Of course. You'll rarely be so lucky as to have your basis factors also be eigenvectors.

这一场噩梦 当然可以 你很少会这么幸运 因为你因子也向。

The fact that there are no real number solutions indicates that there are no eigen vectors.

没有实数解事实表明没有向。

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

唯一值2 但平面上每个向都具有这个值向。

What this expression is saying is that the matrix vector product A times V gives the same result as just scaling the eigenvector V by some value lambda.

这个表达式说矩阵向乘积A乘以V得到结果和向V乘以某个值一样。

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 下一集关于向和值视频 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与这些主题中一个不稳定础有关 而不与向和值本身有关。

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

x轴上所有向都值为1向 因为它们保持固定。

Take a look at what happens if our basis vectors just so happened to be eigenvectors, e.g.

看一下如果向恰好向会发生什么。

What we want is a non zero eigenvector.

我们要一个非零向。