Arrange the following matrix decomposition techniques in the correct order of their application for simplifying a complex matrix in data analysis: A) Eigen Decomposition, B) Singular Value Decomposition, C) Matrix Reconstruction, D) Data Interpretation.

In data analysis we first apply Singular Value Decomposition to split the complex matrix into orthogonal singular vectors and singular values, which highlights the most important directions. Next we perform Eigen Decomposition on the resulting covariance or Gram matrix to identify the principal eigenvectors that capture the main variance. After that we reconstruct an approximate matrix by keeping only the largest singular values and corresponding eigenvectors, which reduces noise and dimensionality. Finally we interpret the simplified matrix to extract meaningful patterns about the data. For example, a 3×3 matrix [[2,0,0],[0,3,0],[0,0,1]] can be SVD‑decomposed, its 2×2 submatrix eigenvalues computed, reconstructed with the two largest singular values, and the resulting pattern shows that the second dimension dominates.