HAL (France)
open access
Ensuring fairness in correspondence analysis
Fairness in unsupervised learning remains underexplored despite its critical importance for preventing the amplification and propagation of social biases in data representations. This work addresses fairness in Correspondence Analysis (CA) and Multiple Correspondence Analysis (MCA), fundamental unsu
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