The Rank of a Matrix

You can think of an $r \times c$ matrix as a set of $r$ row vectors, each having $c$ elements; or you can think of it as a set of $c$ column vectors, each having $r$ elements.

The rank of a matrix is defined as (a) the maximum number of linearly independent column vectors in the matrix or (b) the maximum number of linearly independent $row$ vectors in the matrix. Both definitions are equivalent.

For an $r \times c$ matrix,

• If $r$ is less than $c$, then the maximum rank of the matrix is $r$.
• If $r$ is greater than $c$, then the maximum rank of the matrix is $c$.

The rank of a matrix would be zero only if the matrix had no elements. If a matrix had even one element, its minimum rank would be one.

For example, the following matrix has rank of 2.

$X = \begin{pmatrix} 1 & 2 & 4 & 4 \\ 3 & 4 & 8 & 0 \end{pmatrix}$

Singular Value Decomposition

Formula

$A_{[m \times n]}=U_{[m \times r]} \Sigma_{[r \times r]} (V_{[n \times r]})^T$

• A: Input data matrix
  • $m \times n$ matrix (e.g. m documents, n terms)
• U: left singular vectors
  • $m \times r$ matrix (m documents, r concepts)
• $\Sigma$: Singular values
  • $r \times r$ diagonal matrix (strength of each concept) (r: rank of matrix A)
• V: Right singular vectors
  • $n \times r$ matrix (n terms, r concepts)

Properties

It is always possible to decompose a real matrix A into $A=U \Sigma V^T$, where

• $U, \Sigma, V$: unique
• $U, V$: column orthonormal
  • $U^T U = I$; $V^T V = I$ (I: identity matrix)
  • (Columns are orthogonal unit vectors)
• $\Sigma$: diagonal
  • Entries (singular values) are positive, and sorted in decreasing order ($\sigma_1 \geq \sigma_2 \geq \cdots \geq 0$)

Interpretation

• U: user-to-concept similarity matrix
• V: movie-to-concept similarity matrix
• $\Sigma$: its diagonal elements strength of each concept