Objective-Induced Bias and Search Dynamics in Multiobjective Unsupervised Feature Selection

ArXi:2605.21561v1 Announce Type: new Unsupervised feature selection is commonly formulated as a multiobjective optimisation problem that jointly optimises subset quality and subset size. Yet the behaviour of this formulation depends critically on the choice of evaluation objective, the direction of subset-size regularisation, and the initialisation strategy. We study these factors in a controlled setting using a synthetic dataset with known informative, redundant, and irrelevant feature types.