You are preparing a 250,000 × 150,000 TF-IDF document-term matrix stored in CSR sparse format. The downstream model is an SGD-optimized linear classifier that applies an L2 penalty and assumes all numeric features are on comparable scales. Because of memory limits, centering the data is not an option-any operation that alters zero entries would densify the matrix and make the process infeasible. Which scaling technique is the most appropriate to meet the model's requirements while preserving sparsity?
Apply standard z-score scaling that subtracts the mean and divides by the standard deviation for every feature.
Scale each column with a MaxAbs scaler so its maximum absolute value becomes 1 while zeros remain unchanged.
Use a Robust scaler that subtracts the median and divides by the interquartile range of each feature.
Transform the data with a MinMax scaler to map every feature into the interval .
MaxAbs scaling divides every feature by its maximum absolute value, scaling them into the range [-1, 1] without shifting the data. Because it performs no centering, all explicit zeros remain zeros, so the matrix stays sparse and the transformation is memory-efficient-exactly what is needed for very large TF-IDF inputs. Standard z-score scaling with mean removal would break sparsity by inserting non-zero offsets into every row. Robust scaling with centering enabled also destroys sparsity and cannot be fitted directly to sparse inputs. MinMax scaling subtracts the per-feature minimum; even when that minimum is zero for most TF-IDF columns, any non-zero minimum introduces a shift that densifies those columns. Unlike MaxAbs scaling, it rescales features to a strictly positive range, like , that may hurt the convergence of algorithms expecting zero-centered data.
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Why is MaxAbs scaling preferred for sparse matrices?
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How does MaxAbs scaling compare to z-score scaling for sparse data?
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What would happen if a MinMax scaler were applied to TF-IDF data?