technoinstdheva26969/pseudocode-generator · Pseudo Code for Matrix Decomposition and Standardization Process Input Data matrix A (m x n) where: m = number of rows (data points) n = number of columns (variables/features) Output Standardized output matrix (m x n) Steps Start Initialize matrix A with the input data A contains noisy or raw data (e.g., from experiments or sensors) Perform Singular Value Decomposition (SVD) on matrix A Decompose A into three matrices: U (m x m) - Left Singular Vectors S (m x n) - Singular Values (as a diagonal matrix) V (n x n) - Right Singular Vectors Note: SVD provides components needed for dimensionality reduction and feature extraction Compute Singular Values from the SVD output Extract diagonal elements of S which represent the importance of each principal component Construct the Output Matrix using the components from SVD Multiply U, S, and V to reconstruct an approximation of A This step aids in reducing noise or extracting significant features Recast (reshape) the data matrix if necessary If needed, reorganize the data matrix for analysis or visualization Create features from the Output Matrix Extract relevant features (e.g., principal components or factors) for further analysis Compute the Covariance Matrix of the data The covariance matrix measures relationships between different variables in the dataset Compute Eigenvectors and Eigenvalues of the Covariance Matrix Eigenvectors represent directions of maximum variance (Principal Components) Eigenvalues indicate magnitude of variance along those directions Standardize the variables in the matrix Normalize the data: Subtract the mean from each variable Divide by the standard deviation This ensures comparability of features Construct the Standardized Output Matrix using the adjusted variables This matrix contains standardized features ready for analysis Return the Standardized Output Matrix The standardized matrix is used for downstream analysis (e.g., regression, classification, clustering) End

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