if a matrix is a transformation, then how data can be interpretated in terms of linear mapping?We can interpret a matrix as a linear mapping in linear algebra using matrix representation. However, in machine learning or deep learning, we represent input data as a matrix. Then, how can this data be interpreted in terms of linear mapping?

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If X is   n  ×  p where n is the number of samples and p is the number of features (a.k.a. covariates or predictors), then each column of X is a vector in             R        n   that represents your data&#039;s information about a particular feature. Then you could interpret X as mapping the first standard basis vector (1,0,…,0) to the first feature vector, the second standard basis vector (0,1,0,…,0) to the second feature vector and so on.This interpretation of X as a mapping is not particularly useful, but thinking about the column space as a subspace of             R        n   is useful when thinking about linear regression, PCA, etc.

if a matrix is a transformation, then how data can be interpretated in terms of linear mapping? We

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