485 citations · 735 across the 8 of their papers we have counts for
8 papers
Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection
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…
LDReg: Local Dimensionality Regularized Self-Supervised Learning
Hanxun Huang, Ricardo J. G. B. Campello, Sarah Monazam Erfani +3
Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality…
Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis
Alastair Anderberg, James Bailey, Ricardo J. G. B. Campello +4
We present a nonparametric method for outlier detection that takes full account of local variations in intrinsic dimensionality within the dataset. Using the theory of Local Intrin…
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,…
Subspace Determination through Local Intrinsic Dimensional Decomposition: Theory and Experimentation
Ruben Becker, Imane Hafnaoui, Michael E. Houle +2
Axis-aligned subspace clustering generally entails searching through enormous numbers of subspaces (feature combinations) and evaluation of cluster quality within each subspace. In…
Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality
Sukarna Barua, Xingjun Ma, Sarah Monazam Erfani +2
Generative Adversarial Networks (GANs) are an elegant mechanism for data generation. However, a key challenge when using GANs is how to best measure their ability to generate reali…