# Computational Complexity of PCA

• Step 2: Compute the covariance matrix Cv = X′ c ∗Xc: The covariance matrix is computed by multiplying the mean-centred matrix Xc with its transpose. This is a computationally intensive step because it requires multiplying two matrices where typically none of them can fit in memory.
• Step 3: Compute the eigenvalue decomposition of Cv: Eigenvalues λi and eigenvectors vi satisfy the relation: Cv ∗ λi = λi ∗ vi, where λi is i th eigenvalue and vi is its corresponding eigenvector. Putting the above formula in the matrix form: Cv ∗Λ = Λ∗V, where Λ is a diagonal matrix whose elements are the eigenvalues of the covariance matrix Cv, and V is the matrix whose columns are the corresponding eigenvectors. The eigenvalue decomposition of the matrix Cv is given by Cv = Λ∗V ∗Λ −1.
• Step 4: Get the principal components: The principal components of the input matrix X are the eigenvectors associated with the largest eigenvalues. Since the eigenvalues in the diagonal matrix Λ are sorted in decreasing order, then the first d vectors in the matrix V are the principal components of X: V = (v1, v2,…, vd).

# Time Complexity:

The algorithm has two computationally intensive steps:

1. Computing the eigenvalue decomposition of the covariance matrix

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