Impact of metrics on biclustering solution and quality: A review
To understand how subspace clustering algorithms discover distinct bicluster types and how
their effectiveness has been validated, we offer a systematic literature review on available …
their effectiveness has been validated, we offer a systematic literature review on available …
Bregman power k-means for clustering exponential family data
Recent progress in center-based clustering algorithms combats poor local minima by implicit
annealing through a family of generalized means. These methods are variations of Lloyd's …
annealing through a family of generalized means. These methods are variations of Lloyd's …
On consistent entropy-regularized k-means clustering with feature weight learning: Algorithm and statistical analyses
Clusters in real data are often restricted to low-dimensional subspaces rather than the entire
feature space. Recent approaches to circumvent this difficulty are often computationally …
feature space. Recent approaches to circumvent this difficulty are often computationally …
Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification
The problem of linear predictions has been extensively studied for the past century under
pretty generalized frameworks. Recent advances in the robust statistics literature allow us to …
pretty generalized frameworks. Recent advances in the robust statistics literature allow us to …