We lifted 7 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| muradtuk/projectiveclusteringcoresets | canonical | 5 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| Caratheodory | Ran | muradtuk/projectiveclusteringcoresets/helper_functions.py code served (permissive licence) · get_code("e8784985ae3af9e7") |
| Fast_Caratheodory | Ran | muradtuk/projectiveclusteringcoresets/helper_functions.py code served (permissive licence) · get_code("012c7aeb7fed5a55") |
| determineUpperBound | Ran | muradtuk/projectiveclusteringcoresets/Utils.py code served (permissive licence) · get_code("dab47e37b68503e7") |
| getIdxsOfEllements | Ran | muradtuk/projectiveclusteringcoresets/Utils.py code served (permissive licence) · get_code("c4352b587af8f394") |
| isPD | Ran | muradtuk/projectiveclusteringcoresets/ProjectiveClusteringCoreset.py code served (permissive licence) · get_code("b34bc45e5b7b9ea1") |
| computeInitialWeightVector | Not yet run | muradtuk/projectiveclusteringcoresets/ProjectiveClusteringCoreset.py code served (permissive licence) · get_code("1e65f9a565b504a6") |
| train_model | Not yet run | muradtuk/projectiveclusteringcoresets/helper_functions.py code served (permissive licence) · get_code("3f99d68cc2c85872") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
$(j,k)$-projective clustering is the natural generalization of the family of $k$-clustering and $j$-subspace clustering problems. Given a set of points $P$ in $\mathbb{R}^d$, the goal is to find $k$ flats of dimension $j$, i.e., affine subspaces, that best fit $P$ under a given distance measure. In this paper, we propose the first algorithm that returns an $L_\infty$ coreset of size polynomial in $d$. Moreover, we give the first strong coreset construction for general $M$-estimator regression. Specifically, we show that our construction provides efficient coreset constructions for Cauchy, Welsch, Huber, Geman-McClure, Tukey, $L_1-L_2$, and Fair regression, as well as general concave and power-bounded loss functions. Finally, we provide experimental results based on real-world datasets, showing the efficacy of our approach.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2203.04370")
get_code_for_paper("2203.04370")
have("2203.04370")
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