# All the basic Matrix Algebra you will need in Data Science All the basic Matrix Algebra you will need in Data Science
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Matrix Basic Definitions
A matrix A over a field K or, simply, a matrix A (when K is implicit) is a rectangular array of scalars usually presented in the following form:
The rows of such a matrix A are the m horizontal lists of scalars:
and the columns of A are the n vertical lists of scalars:
A matrix with m rows and n columns is called an m by n matrix, written as m*n. The pair of numbers m and n is called the size of the matrix. Two matrices A and B are equal, written A = B, if they have the
same size and if corresponding elements are equal. Thus, the equality of two m*n matrices is equivalent to a system of mn equalities, one for each corresponding pair of elements.
Matrix vs Vectors
A matrix is simply a rectangular array of numbers and a vector is a row (or column) of a matrix.
vector is one dimension array such a=[1 2 3 4 5], but the matrix is more than one dimension array, and has some of operations.
A real system of linear equations
can be simply rewritten using real vector and matrix symbols as a real matrix
equation
The rank of a matrix
Inverse Matrix
The following are properties of the conjugate, transpose, conjugate transpose,
and inverse matrices.
The matrix conjugate, transpose, and conjugate transpose satisfy the distributive law:
2. The transpose, conjugate transpose, and inverse matrix of product of two matrices satisfy the following relationship:
3. Each of the symbols for the conjugate, transpose, and conjugate transpose can be exchanged with the symbol for the inverse:
Let A and B, be two matrices with the same size, say m *n matrices. The sum of A and B, written A þ B, is the matrix obtained by adding corresponding elements from A and B. That is,
By using this definition, it is easy to verify that the addition and subtraction of
two matrices obey the following rules:
The product of the matrix A by a scalar k
The product of the matrix A by a scalar k, written k . A or simply kA, is the matrix obtained by multiplying each element of A by k. That is,
Observe that A +B and kA are also m*n matrices. We also define
Matrix Multiplication
The product AB of a row matrix A, and a column matrix B, with the same number of elements, is defined to be the scalar (or 1*1 matrix) obtained by multiplying corresponding entries and adding; that is,
Suppose A, and B, are matrices such that the number of columns of A is
equal to the number of rows of B; say, A is an m * p matrix and B is a p*n matrix. Then the product AB is the m * n matrix whose ij-entry is obtained by multiplying the ith row of A by the jth column of B. That is,
where
There are four simple rules that will help us in multiplying matrices, listed here:
1. Firstly, we can only multiply two matrices when the number of columns in matrix A is equal to the number of rows in matrix B.
2. Secondly, the first row of matrix A multiplied by the first column of matrix B gives us the first element in the matrix AB, and so on.
3. Thirdly, when multiplying, order matters — specifically, AB ≠ BA.
4. Lastly, the element at row i, column j is the product of the ith row of matrix A and the jth column of matrix B.
Further read through this for a very nice visual flow of Matrix Multiplication.
Let A; B; C be matrices. Then, whenever the products and sums are defined,
Transpose of a Matrix
The transpose of a matrix A, written A^T , is the matrix obtained by writing the columns of A, in order, as rows.
In other words, if
Some common Theorems that apply to Matrix Transpose
Let A and B be matrices and let k be a scalar. Then, whenever the sum and product are defined,
Square Matrices
A square matrix is a matrix with the same number of rows as columns. An n *n square matrix is said to be of order n and is sometimes called an n-square matrix.
Recall that not every two matrices can be added or multiplied. However, if we only consider square matrices of some given order n, then this inconvenience disappears. Specifically, the operations of addition, multiplication, scalar multiplication, and transpose can be performed on any n*n matrices, and
the result is again an n — n matrix.
The following are square matrices of order 3
And see all the following Matrices obtained after addition of multiplications fo A and B are also of order 3
Diagonal and Trace
Let A, be an n-square matrix. The diagonal or main diagonal of A consists of the elements with the same subscripts — that is,
The trace of A, written tr(A), is the sum of the diagonal elements. Namely,
The following theorem applies on Trace of Matrices
Eigenvalues and Eigenvectors
For an n × n matrix A, if the linear algebraic equation
has a nonzero n × 1 solution vector u, then the scalar λ is called an eigenvalue of the matrix A, and u is its eigenvector corresponding to λ
Eigenvalues and eigenvectors are only for square matrices. Non-square matrices do not have eigenvalues. If the matrix A is a real matrix, the eigenvalues will either be all real, or else there will be complex conjugate pairs.
Eigenvectors are by definition nonzero. Eigenvalues may be equal to zero.
We do not consider the zero vector to be an eigenvector: since A0=0=λ0 for every scalar λ, the associated eigenvalue would be undefined.
If someone hands you a matrix A and a vector v, it is easy to check if v is an eigenvector of A: simply multiply v by A and see if Av is a scalar multiple of v. On the other hand, given just the matrix A it is not so straightforward to find the eigenvectors. There are few steps involved here.
MATRIX FACTORIZATION
First note, most of the times “matrix factorization” and “matrix decomposition” are used interchangeably
Suppose we want to express an m * n matrix A as the product of two matrices A1 and A2 in the form
where A1 and A2 are m * r and r * n matrices, respectively. We assume r m, n. We call this problem matrix factorization.
If no special dependencies exist among columns and rows of a matrix, its rank generally coincides with the smaller of the numbers of columns and rows. Assume that matrices A1 and A2 both have rank r . It is known that the rank of the product of two matrices does not exceed the rank of either one. Namely,
for any matrices A and B for which their product can be defined.
Singular Value Decomposition
Singular value decomposition is a method of decomposing a matrix into three other matrices and its a central matrix decomposition method in linear algebra. It has been referred to as the “fundamental theorem of linear algebra”.
In a slightly detailed form
The diagonal entries σi , i = 1, . . . , r, of Σ are called the singular values,
ui are called the left-singular vectors, and v j are called the right-singular
vectors. By convention, the singular values are ordered, i.e., σ1 > σ2 >
σr > 0.
The columns of U are called left singular vectors, while those of V are called right singular vectors.
We know that U and V are orthogonal, that is:
Where I is the identity matrix. Only the diagonals of the identity matrix are 1, with all other values being 0. Note that because U is not square we cannot say that U ^Transpose(U)=I, so U is only orthogonal in one direction.
In another form for the orthogonality of U and V
The SVD is used widely both in the calculation of other matrix operations, such as matrix inverse, but also as a data reduction method in machine learning. SVD can also be used in least squares linear regression, image compression, and denoising data.
Here are the dimensions of the factorization:
Interpretation of SVD
The intuition behind the singular value decomposition needs some explanations about the idea of matrix transformation.
A is a matrix that can be seen as a linear transformation. This transformation can be decomposed in three sub-transformations: 1. rotation, 2. re-scaling, 3. rotation. These three steps correspond to the three matrices U, Σ, and V.
Comparing SVD with Eigenvalues and Eigen Vectors
First noting the formulae of Eigenvalues
And the one for SVD
Source
The key point to note here, that the concept of Singular-Value is a lot like eigenvalues, but different because the matrix A now is more usually rectangular. But for a rectangular matrix, the whole idea of eigenvalues
is not possible because if I multiply A times a vector x in n dimensions, out will come something in m dimensions and it’s not going to equal lambda x.
So Ax equal lambda x is not even possible if A is rectangular. And here comes SVD and so this is the new word is singular. And in between go the —
not the eigenvalues, but the singular values. There are two sets of singular vectors, not one. For eigenvectors, we just had one set.
We’ve got one set of left eigenvectors in m dimensions, and we’ve got another set of right eigenvectors in n dimensions. And numbers in between are not eigenvalues, but singular values.
Derivatives of Scalars with Respect to Vectors; The Gradient
The derivative of a scalar-valued function with respect to a vector is a vector
of the partial derivatives of the function with respect to the elements of the
vector. If f (x) is a scalar function of the vector x = (x1 , . . . , xn ),
if those derivatives exist. This vector is called the gradient of the scalar-valued
function, and is sometimes denoted by
The notation g_f or ∇f implies differentiation with respect to “all” arguments
of f , hence, if f is a scalar-valued function of a vector argument, they represent
a vector. This derivative is useful in finding the maximum or minimum of a func-
tion. Such applications arise throughout statistical and numerical analysis.
Derivatives of Vectors with Respect to Vectors; The Jacobian
The derivative of an m-vector-valued function of an n-vector argument con-
sists of nm scalar derivatives. These derivatives could be put into various structures. Two obvious structures are an n × m matrix and an m × n matrix.
Some more notes on Jacobian and ways to express Jacobian operator
Jacobian operator with respect to an m × n matrix X is defined as
and Jacobian matrix of the real scalar function f (X) with respect to its matrix
variable X
is given by
Higher-Order Derivatives with Respect to Vectors; The Hessian
Higher-order derivatives are derivatives of lower-order derivatives. As we have seen, a derivative of a given function with respect to a vector is a more complicated object than the original function. The simplest higher-order derivative with respect to a vector is the second-order derivative of a scalar-valued function. Higher-order derivatives may become uselessly complicated.
In accordance with the meaning of derivatives of vectors with respect to
vectors, the second derivative of a scalar-valued function with respect to a
vector is a matrix of the partial derivatives of the function with respect to the
elements of the vector. This matrix is called the Hessian, and is denoted by
Hf or sometimes by ∇∇f or ∇^2f :
Formulas for Some Matrix Derivatives