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Found 4 papers, 0 papers with code
A Finite-Horizon Approach to Active Level Set Estimation
We present a finite-horizon search procedure to perform LSE in one dimension while optimally balancing both the final estimation error and the distance traveled for a fixed number of samples.
Subspace Clustering using Ensembles of $K$-Subspaces
We present a novel geometric approach to the subspace clustering problem that leverages ensembles of the K-subspaces (KSS) algorithm via the evidence accumulation clustering framework.
Leveraging Union of Subspace Structure to Improve Constrained Clustering
We demonstrate on several datasets that our algorithm drives the clustering error down considerably faster than the state-of-the-art active query algorithms on datasets with subspace structure and is competitive on other datasets.
Distance-Penalized Active Learning Using Quantile Search
Adaptive sampling theory has shown that, with proper assumptions on the signal class, algorithms exist to reconstruct a signal in $\mathbb{R}^{d}$ with an optimal number of samples.
