Linear Algebra for Machine Learning | by Luay Matalka | Dec, 2020


When coping with actual numbers, 1 is the identification for multiplication. In different phrases, for any actual quantity z, the #1 occasions z will equivalent z occasions 1, which can equivalent z. So the identification assets of one implies that any actual quantity z multiplied by 1 is the same as z, thus permitting z to stay its identification.

multiplicative identification

We even have identification matrices. In different phrases, for any matrix A, there can be an identification matrix, I, that once multiplied with matrix A, equals to matrix A.

AI = IA = A

Identity matrices are sq. matrices, which means that the selection of rows equals the selection of columns. Identity matrices are denoted by I, infrequently as Inxn, with nxn being the measurement of the identification matrix. For a matrix of the measurement mxn, its identification matrix would be the measurement nxn, which must make sense, since for the ones matrices to be multiplied, the selection of columns of the primary matrix will have to equivalent the selection of rows in the second one matrix.

Examples of identification matrices:

identification matrices

As we will see above, identification matrices (with the exception of for when n=1), have 1’s alongside the diagonals, and zeros far and wide else.

As mentioned ahead of, generally, matrix multiplication isn’t commutative, until this is a matrix multiplied by its identification matrix.

Note: Since A is an mxn matrix, then the identification matrix at the left has the size nxn, however within the heart, the identification matrix has the measurement mxm, for the reason that matrix A is first within the matrix-matrix multiplication.

Using geometric instinct, we will recall to mind an identification matrix as now not inflicting a linear transformation. Thus, the order does now not topic, since in both state of affairs (A*I or I*A), the outcome would be the linear transformation encoded in matrix A.


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