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## Linear algebra

### Course: Linear algebra > Unit 2

Lesson 2: Linear transformation examples# Expressing a projection on to a line as a matrix vector prod

Expressing a Projection on to a line as a Matrix Vector prod. Created by Sal Khan.

## Want to join the conversation?

- 12:44I notice that the determinant of A is 0. Is this just a coincidence or does it mean something?(10 votes)
- Yes, the determinant is 0. That actually shows that the image of the transformation isn't all of ℝ², which we already knew from the fact that the image will always be a line.(9 votes)

- Sal says he previously discussed unit vectors in an earlier video. I have faithfully followed the list and never saw it. Has it been removed or relocated to a different list?

Thanks(3 votes)- Here is a link to his video, "Unit Vectors" in the Linear Algebra playlist:

http://www.khanacademy.org/math/linear-algebra/v/unit-vectors

Also, there is more discussion of unit vectors in various videos of the Physics playlist.

Hope this helps.(18 votes)

- I don't get why Sal can do the thing with the Identity matrix. He applies the transformation to one column at a time, and the columns he does it to it the identity matrix. Why does this work? (Did he do it in a previous video?)(3 votes)
- The proof comes from this video: https://www.khanacademy.org/math/linear-algebra/matrix_transformations/linear_transformations/v/linear-transformations-as-matrix-vector-products

We are using the identity matrix to do the transformation dimension by dimension

If we multiply the vector x by the identity matrix before we do the transformation, we can rewrite Tx as a matrix vector product

Tx =

T[ x1 (1 0) x2 (0 1)] =

[ T (1 0) T(0 1) ] (x1 x2) =

[ T (1 0) T(0 1) ] x

In this lesson we are using the projection as our transformation. Sal skips the part where he breaks the x vector into components and multiplies the components by the identity matrix. He starts with finding the projection matrix and writes the projection as Tx later.(6 votes)

- This was a wonderful lesson. Among other things, the video shows how to form a matrix that projects any vector onto a line (in 2D). How do I extend this process to form a matrix that projects 3D vectors onto a plane?(2 votes)
- This is covered further on in the Linear Algebra videos. I also highly recommend this video from MIT on projections: http://www.youtube.com/watch?v=Y_Ac6KiQ1t0(7 votes)

- The determinant of the matrix Sal created in this video is 0:

(u1^2 x u2^2) - (u1u2 x u2u1) =

(u1u2)^2 - (u1u2)^2 = 0

Is it another coincidence?(2 votes)- This is not another coincidence. The determinant of a transformation matrix gives the quantity by which the area is scaled. By projecting an object onto a line, we compact the area to zero, so we get a zero determinant.

Having a determinant of zero also means that it is impossible to reverse this operation (since an inverse matrix does not exist). This makes sense, because many points are being mapped to a single point in a projection, which means you lose some information in the process.(5 votes)

- At6:08, Sal asks if the transformation is linear. I have noticed he asks the same question often in other videos. What is the significance of something being linear transformation? What would be the issue if it wasn't linear transformation? Thanks.(2 votes)
- A linear transformation can be defined using a single matrix and has other useful properties. A non-linear transformation is more difficult to define and often lacks those useful properties. Intuitively, you can think of linear transformations as taking a picture and spinning it, skewing it, and stretching/compressing it. Anytime you want those types of effects, linear transformations are wonderful.(5 votes)

- Where has this stuff been all my life? It is so, so cool!(4 votes)
- Wouldn't a projection be a mapping from Rn to Rn-1? Specifically: In your example all the output vectors are on a line, so wouldn't you be in R1?(2 votes)
- Remember that a line in

is a**R**²*subset*of

, and hence**R**²*in*

. Whether you consider any line in**R**²

to**R**²*be*

, you can decide for yourself (see the Cantor-Dedekind axiom: http://en.wikipedia.org/wiki/Cantor–Dedekind_axiom). Anyhow, the conclusion is that a line in**R**¹

is in**R**²

.**R**²(3 votes)

- Why is it useful to know the projection of something on to a line? And what if the vector that we want to find the projection for lies on the other side of the line than what was shown in the video? Is there any projection at all in that case?(2 votes)
- The projection of a vector onto another is very closely related to linear independence, in R^2 if two vectorx, say x and y, are linearly independent then we me express the space R^2 as the span{x,y} = span(x) + span(y). That is we may decompose the space into a "direct sum" of the vectors that don't project onto each other. This is what constitutes the Primary Decomposition theorem, which is a powerful theorem in more advanced linear algebra courses! For more information on this, look into the properties of projection maps and the wikipedia page for this(3 votes)

- Dear Sal or any other bright person that can answer this question,

With this method of defining the projection on a line, do you always have an orthogonal projection? Or is it also possible to get an oblique projection (i.e. not orthogonal)?

Thank you!(1 vote)- Yes, you always have an orthogonal projection, because it is defined such that the dot product between any vector lying on the line and the difference between the original vector and its projection is equal to 0.(4 votes)

## Video transcript

In the last video we saw that
if we had some line that was defined as all of the scalar
multiples of some vector-- I'll just write it like this. The scalar multiples,
obviously, are any real number. We defined a transformation, and
I didn't speak of it much in terms of transformations, but
it was a transformation. We defined a projection
onto that line L as a transformation. In the video, we drew it as
transformations within R2, but it could be, in general, a
transformation from Rn to Rn. We defined it as, the projection
of x onto L was equal to the dot product of x,
with this defining vector. x dot this defining vector,
divided by that defining vector dotted with itself. All of that times the defining
vector of the line. This was our definition. A couple of things might have
popped out at you right when we first saw this. When you dot a vector with
itself, what's that equal to? We know that if I take some
vector, and I dot it with itself, that is equivalent
to the length of the vector squared. We can rewrite this as being
equal to x dot v, over the length of v squared, all
of that times v. Wouldn't it be nice if the
length of v was 1. The length of v was
equal to 1. If the length of v was 1, or
this is another way of saying that, v is a unit vector. Our formula for our projection
would just simplify to x dot v. All of that times, this will
just be some scalar number, that times v. You say, hey Sal, how do
we know if this is a unit vector or not. What you can realize is that
any-- Let me draw it this way. When I drew it in the previous
video, I just picked a line, like that. The line can be really defined
to this vector v in the line. It can be any of the vectors
that's contained in the line. The vector v could
be like that. Let's say someone gives
you a vector v that isn't a unit vector. Let's say that the length
of v is not equal to 1. How can you define a line
using sum unit vector. You can just normalize v. You can define sum unit
vector right here. You could define sum
vector right there. Let's call it u, and I'll
say it's a unit vector. That is equal to 1 over 1 over
the length of v times v. I showed you this in the
unit vector video. You can construct a unit vector
that goes in the same direction as any vector,
essentially just by dividing, or I guess multiplying,
that vector times 1 over its length. In general, we can always
redefine the line. All of the possible scalar
multiples of v are going to be the same thing as all of the
scalar multiples of our unit vector, u, which is just
a scalar multiple of v. We can redefine our line. If we redefine our line, L,
as being equal to all the possible scalar multiples of
our unit vector, where the scalars are any members
of the real numbers. Our projection definition
simplifies a good bit. The projection of x onto L
becomes x dot our unit vector, times the unit vector, times
the unit vector itself. That case that I did in the
previous video, where I had those two vectors. Where I said the vector v that
defined the line, I think it was vector 2, 1. Our vector x was
equal to 2, 3. If you want to do this
definition, we just have to turn this guy into a
unit vector first. The way you turn him into a unit
vector, you figure out the magnitude. In this case the magnitude
of v is equal to what. 2 squared plus 1 squared is 1. You take the square
root of that. Let me just write. It's equal to square root of 2
squared plus 1 squared, which is equal to the square
root of 5. You can define your u-- your
unit vector could just be 1 over this, times that guy. 1 over the square root
of 5 times 2, 1. You could multiply
it out, or not. You could just leave
it in this form. You can always, for any vector
v, you can always find a unit vector that goes in the same
direction, assuming that we're dealing with non-zero vectors. You can always reduce anything
like this, to some other definition, like this. Where this is a unit
vector version of your vector v up there. I just said that,
look, this is a transformation from Rn to Rn. The one thing that we're not
sure of, is this a linear transformation. We can always write
it like this. Let's see if this is always
going to be a linear transformation. There's two conditions for it to
be a linear transformation. Let's see what happens if I take
the projection onto L of two vectors. Let's say the vector a
plus the vector v. If I take the sum of
their vectors. If this is a linear
transformation, this should be equivalent to taking each
of their projections individually, and
then summing. Let's see if this is the case. This is equal to, by our
definition, we'll use the unit vector version, because
it's simpler. This is equal to a plus b,
that's our x, dot u. And then, all of that times
our unit vector. We know that the dot product has
a distributive property, so that this is equal to
a dot u plus b dot u. These are unit vectors. All of that times
the vector u. These are just scalar numbers. So scalar multiplication has
distributive properties. This is equal to a dot u,
times our vector u. Remember, this is just going
to be some scalar. Plus b dot u times our
unit vector u. What is this equal to. This right here is equal
to the projection of a. This is equal to the projection
of a onto L, by definition, right here. By this definition. If we assume that we're dealing
with the unit vector definition for the line. This is equal to, this whole
thing right here, is equal to plus the projection onto
L of the vector b. We see our first condition
for this being a linear transformation holds. A projection of the sum of the
vectors is equal to the sum of the projections of
the vectors. Our second condition is that
the projection of a scalar multiple should be equal to
a scalar multiple of the projection. Let me write that down. What is the projection onto L
of some scalar multiple of some vector a. That is equal to ca dot
our unit vector u times unit vector u. This one is a little bit
more straightforward. This is the scalar multiple. We see it in our dot product
properties, this is equal to c times a dot u, times
the vector u. This is just equal to c times,
this right here, is a projection of a onto L. We met both of our conditions
for linear transformations. We know that our projection
onto a line L in Rn is a linear transformation. That tells us that we can
represent it as a matrix transformation. We know that projection of x
onto L, we already know this definition, it can
be rewritten. It doesn't hurt to rewrite it. As x dot some unit vector
that defines our line. Let me draw it with a little
hat to show that it is a unit vector. Times the unit vector
itself, so that we actually get a vector. How can I write this as
some matrix product. Some matrix vector product. I want to write it as
a product of some matrix times x. To simplify things, since we're
actually dealing with a matrix, let's limit ourselves
to the case of R2. I'm assuming that my projection
onto L is going to be a mapping from R2 to R2. You could do what I'm
doing here with an arbitrary dimension. If we're doing it in R2, then
our matrix A, right there, is going to be a 2 by 2 matrix. We've seen in multiple videos
that to figure out the matrix A, we just take the identity
matrix that has the standard basis vectors as columns. 0, 1. Or 1, 0, and then 0, 1. And we apply the transformation
to each of these columns. We could say that A is going
to be equal to-- its first column is going to be equal to
the projection onto L of this thing right here. We'll do it in this orange
color, right here. What is that going to be. That is going to
be this dot u. Let me write my u. My unit vector, let's just
assume that u can be rewritten as my unit vector is equal
to sum u1 and u2. Just like that. I need to take this dot
my unit vector, let me write this down. Let me write this on the side. The first thing I want to do
is figure out what the projection-- the projection
onto L, let me write it this way. We know the projection is just
equal to this dot this times that vector. Let me write that. The vector 1, 0 dot
the unit vector u, which is just u1, u2. We're going to have that
times my unit vector. I'll write it like this. Times the vector u1, u2. This is going to be my
first column in my transformation matrix. My second column is going to be
the same thing, but I'm not ready to take the projection
of this guy. The definition of our projection
is you dot this guy with our unit vector. So we dot it. We're taking the dot
product of 0, 1. 0, 1 dot my unit vector
dot u1, u2. I'm going to multiply
that times my unit vector, times u1, u2. This seems very complicated, but
it should simplify when we actually try to work out our
transformation matrix. Let's do it. When I dot these two guys,
what do I get. Let me write it here. My matrix A will become 1 times
u1, plus 0 times u2. That's just u1. This whole thing just simplifies
to u1, when I take the dot product of
these two things. Times u1, u2. That's going to be
my first column. My second column, if I dot these
two guys, I get 0 times u1 plus 1 times u2. So I'm going to get u2 times
my unit vector, u1, u2. If I multiply that out, this
will be equal to what. I can just write them
as columns. u1 times u1 is u1 squared. u1 times u2 is u1, u2. u2 times u1 is u2 times u1. Then, u2 times u2
is u2 squared. You give me any unit vector
and I will give you the transformation that gives you
any projection of some other vector onto the line
defined by that. That was a very long
way of saying that. Let's go back to what
I did before. Let's say we want to find any
projection onto the line, onto the vector, I'll draw it here. We'll do the same example that
we did in the last video. If I have some vector v
that looks like that. We said the vector v was equal
to the vector 2, 1. That was my vector v. How can we find sum
transformation for the projection onto the
line defined by v? Onto this line right here. The line defined by v. What we can first do is convert
v into a unit vector. We can convert v into
a unit vector that goes in the same direction. Some unit vector u. We did that already up here. Where we essentially just
divided [? bv ?] by it's length. Let's take v and divide
by it's length. The unit vector is this, 1
over the square root of 5 times our vector v. It was 1 over the square
root of 5 times our vector v, right there. You start with a unit
vector there. You just create this matrix,
and then we will have our transformation matrix. If this is our u, what will
our matrix be equal to. This is u. Then our matrix would be
equal to u1 squared. What is u1 squared. Let me rewrite our u a little
bit, not at angles. Our vector u, our unit vector
that defines this line, is equal to the vector 2 over the
square root of 5 and 1 over the square root of 5. I just multiplied
out this scalar. If we want to construct this
matrix, we get A is equal to u1 squared. What is this squared? It becomes 2 squared 4 over the
square root of 5 squared, which is just 5. Equals 4 over 5. What is u1 times u2? 2 times 1 over square root of
5 times square root of 5. So, 2/5. I just multiplied these two. What is u2 times u1. Same thing. Order doesn't matter
when you multiply. This will also be 2/5. What is u2 squared. 1 squared over the square root
of 5 squared is just 1/5. Now we can say-- and that's the
neat thing about creating these matrices, that the
projection-- Let's say we have some, let's say this is the
origin right here, we have some other vector
x, right here. We can now define our
transformation. The projection onto L where
L is equal to any scalar multiple of our unit vector u. It's right here. Is a member of the reals. That is our line L. The projection onto L
of any vector x is equal to this matrix. Is equal to the matrix 4, 5,
2/5, 2/5, 1/5 times x. Which is a pretty neat result,
at least for me. We once again reduced everything
to just a matrix multiplication. You take this x and you multiply
it by this matrix, you're going to get its
projection onto the L, onto the line. If you take this vector, let's
say a, and you multiply it times this matrix right there,
you're going to get its projection. Its projection onto the line. If you could take this vector--
No, it should go through the origin. I want to draw it in
standard position. If you take this vector, right
there, and multiply it times this matrix, you're going to get
this vector, right here, that is contained in the line. When you subtract it from
this, it's orthogonal. We know the definition. It's kind of the shadow
of that vector. Anyway, I think this
is pretty neat.