机器学习是一门研究在非特定编程条件下让计算机采取行动的学科。最近二十年，机器学习为我们带来了自动驾驶汽车、实用的语音识别、高效的网络搜索，让我们对人类基因的解读能力大大提高。当今机器学习技术已经非常普遍，您很可能在毫无察觉情况下每天使用几十次。许多研究者还认为机器学习是人工智能（AI）取得进展的最有效途径。在本课程中，您将学习最高效的机器学习技术，了解如何使用这些技术，并自己动手实践这些技术。更重要的是，您将不仅将学习理论知识，还将学习如何实践，如何快速使用强大的技术来解决新问题。最后，您将了解在硅谷企业如何在机器学习和AI领域进行创新。 本课程将广泛介绍机器学习、数据挖掘和统计模式识别。相关主题包括：(i) 监督式学习（参数和非参数算法、支持向量机、核函数和神经网络）。(ii) 无监督学习（集群、降维、推荐系统和深度学习）。(iii) 机器学习实例（偏见/方差理论；机器学习和AI领域的创新）。课程将引用很多案例和应用，您还需要学习如何在不同领域应用学习算法，例如智能机器人（感知和控制）、文本理解（网络搜索和垃圾邮件过滤）、计算机视觉、医学信息学、音频、数据库挖掘等领域。
Principal Component Analysis Problem Formulation
Loading...
来自 Stanford University 的课程
机器学习
86224 个评分
从本节课中
Dimensionality Reduction
In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets.
与讲师见面
Andrew NgCEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
For the problem of dimensionality reduction, by far the most popular,
by far the most commonly used algorithm is something called
principle components analysis, or PCA.
In this video, I'd like to start talking about the problem formulation for PCA.
In other words, let's try to formulate,
precisely, exactly what we would like PCA to do.
Let's say we have a data set like this.
So, this is a data set of examples x and R2 and let's say I want to
reduce the dimension of the data from two-dimensional to one-dimensional.
In other words, I would like to find a line onto which to project the data.
So what seems like a good line onto which to project the data,
it's a line like this, might be a pretty good choice.
And the reason we think this might be a good choice is that if you look at where
the projected versions of the point scales, so I take this point and
project it down here.
Get that, this point gets projected here, to here, to here, to here.
What we find is that the distance between each point and
the projected version is pretty small.
That is, these blue line segments are pretty short.
So what PCA does formally is it tries to find a lower dimensional surface,
really a line in this case, onto which to project the data so
that the sum of squares of these little blue line segments is minimized.
The length of those blue line segments,
that's sometimes also called the projection error.
And so what PCA does is it tries to find a surface onto which to project the data so
as to minimize that.
As an aside, before applying PCA, it's standard practice to first
perform mean normalization at feature scaling so that the features x1 and
x2 should have zero mean, and should have comparable ranges of values.
I've already done this for this example, but I'll come back to this later and
talk more about feature scaling and the normalization in the context of PCA later.
But coming back to this example, in contrast to the red line that I just drew,
here's a different line onto which I could project my data,
which is this magenta line.
And, as we'll see, this magenta line is a much worse direction
onto which to project my data, right?
So if I were to project my data onto the magenta line,
we'd get a set of points like that.
And the projection errors, that is these blue line segments, will be huge.
So these points have to move a huge distance in order to
get projected onto the magenta line.
And so that's why PCA, principal components analysis,
will choose something like the red line rather than the magenta line down here.
Let's write out the PCA problem a little more formally.
The goal of PCA, if we want to reduce data from two-dimensional to
one-dimensional is, we're going to try find a vector that is a vector u1,
which is going to be an Rn, so that would be an R2 in this case.
I'm gonna find the direction onto which to project the data, so
it's to minimize the projection error.
So, in this example I'm hoping that PCA will find this vector,
which l wanna call u(1), so that when I project the data onto
the line that I define by extending out this vector,
I end up with pretty small reconstruction errors.
And that reference of data that looks like this.
And by the way, I should mention that where the PCA gives me u(1) or
-u(1), doesn't matter.
So if it gives me a positive vector in this direction, that's fine.
If it gives me the opposite vector facing in the opposite direction, so
that would be like minus u(1).
Let's draw that in blue instead, right?
But it gives a positive u(1) or negative u(1), it doesn't matter because
each of these vectors defines the same red line onto which I'm projecting my data.
So this is a case of reducing data from two-dimensional to one-dimensional.
In the more general case we have n-dimensional data and
we'll want to reduce it to k-dimensions.
In that case we want to find not just a single vector onto which to project
the data but we want to find k-dimensions onto which to project the data.
So as to minimize this projection error.
So here's the example.
If I have a 3D point cloud like this, then maybe what I want to do is find vectors.
So find a pair of vectors.
And I'm gonna call these vectors.
Let's draw these in red.
I'm going to find a pair of vectors, sustained from the origin.
Here's u(1), and here's my second vector, u(2).
And together, these two vectors define a plane, or they define a 2D surface, right?
Like this with a 2D surface onto which I am going to project my data.
For those of you that are familiar with linear algebra, for
this year they're really experts in linear algebra, the formal definition of this is
that we are going to find the set of vectors u(1), u(2), maybe up to u(k).
And what we're going to do is project the data
onto the linear subspace spanned by this set of k vectors.
But if you're not familiar with linear algebra, just think of it as
finding k directions instead of just one direction onto which to project the data.
So finding a k-dimensional surface is really finding a 2D plane in this case,
shown in this figure,
where we can define the position of the points in a plane using k directions.
And that's why for PCA we want to find k vectors onto which to project the data.
And so more formally in PCA, what we want to do is find this way to project the data
so as to minimize the sort of projection distance,
which is the distance between the points and the projections.
And so in this 3D example too.
Given a point we would take the point and project it onto this 2D surface.
We are done with that.
And so the projection error would be, the distance between the point and
where it gets projected down to my 2D surface.
And so what PCA does is I try to find the line, or a plane, or whatever,
onto which to project the data, to try to minimize that square projection,
that 90 degree or that orthogonal projection error.
Finally, one question I sometimes get asked is how does PCA relate to
linear regression?
Because when explaining PCA, I sometimes end up drawing diagrams like these and
that looks a little bit like linear regression.
It turns out PCA is not linear regression, and
despite some cosmetic similarity, these are actually totally different algorithms.
If we were doing linear regression, what we would do would be, on the left we would
be trying to predict the value of some variable y given some info features x.
And so linear regression, what we're doing is we're fitting a straight line so
as to minimize the square error between point and this straight line.
And so what we're minimizing would be the squared magnitude of these blue lines.
And notice that I'm drawing these blue lines vertically.
That these blue lines are the vertical distance between the point and
the value predicted by the hypothesis.
Whereas in contrast, in PCA, what it does is it tries to minimize
the magnitude of these blue lines, which are drawn at an angle.
These are really the shortest orthogonal distances.
The shortest distance between the point x and this red line.
And this gives very different effects depending on the dataset.
And more generally, when you're doing linear regression,
there is this distinguished variable y they we're trying to predict.
All that linear regression as well as taking all the values of x and
try to use that to predict y.
Whereas in PCA, there is no distinguish, or
there is no special variable y that we're trying to predict.
And instead, we have a list of features, x1, x2, and so on, up to xn, and
all of these features are treated equally, so no one of them is special.
As one last example, if I have three-dimensional data and
I want to reduce data from 3D to 2D, so maybe I wanna find two directions,
u(1) and u(2), onto which to project my data.
Then what I have is I have three features, x1, x2, x3, and
all of these are treated alike.
All of these are treated symmetrically and
there's no special variable y that I'm trying to predict.
And so PCA is not a linear regression, and
even though at some cosmetic level they might look related,
these are actually very different algorithms.
So hopefully you now understand what PCA is doing.
It's trying to find a lower dimensional surface onto which to project the data, so
as to minimize this squared projection error.
To minimize the square distance between each point and
the location of where it gets projected.
In the next video, we'll start to talk about how to
actually find this lower dimensional surface onto which to project the data.
