Near-optimal linear predictive clustering in non-separable spaces is achieved through MIP complexity reductions with provable bounds and QPBO approximations that outperform greedy methods in regression error and scalability.
yk . . . yK # and feature variables X=
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Near-optimal Linear Predictive Clustering in Non-separable Spaces via MIP and QPBO Reductions
Near-optimal linear predictive clustering in non-separable spaces is achieved through MIP complexity reductions with provable bounds and QPBO approximations that outperform greedy methods in regression error and scalability.