1 citations · 1 across the 2 of their papers we have counts for
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Unsupervised Feature Selection Using Local Intrinsic Dimensionality -- Extended Version
Oussama Chelly, Michael E. Houle, Ken-ichi Kawarabayashi +2
High dimensionality degrades both the efficiency and the effectiveness of learning algorithms, and in many applications no class labels are available to guide dimensionality reduct…
Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection -- Extended Version
Muhammad Rajabinasab, Michael E. Houle, Oussama Chelly +1
Many novel unsupervised feature selection methods are proposed each year, yet their empirical evaluation is limited to supervised and unsupervised evaluation metrics computed on se…
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis
Laurent Amsaleg, Oussama Chelly, Michael E. Houle +3
Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance in many data mining and machine learning tasks, including dimensionality reduction, outlier detection,…