We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| mperrot/ComparisonHC | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| planted_model | Ran | mperrot/ComparisonHC/resources/utils.py code served (permissive licence) · get_code("da05eb17824521e8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We address the classical problem of hierarchical clustering, but in a framework where one does not have access to a representation of the objects or their pairwise similarities. Instead, we assume that only a set of comparisons between objects is available, that is, statements of the form "objects $i$ and $j$ are more similar than objects $k$ and $l$." Such a scenario is commonly encountered in crowdsourcing applications. The focus of this work is to develop comparison-based hierarchical clustering algorithms that do not rely on the principles of ordinal embedding. We show that single and complete linkage are inherently comparison-based and we develop variants of average linkage. We provide statistical guarantees for the different methods under a planted hierarchical partition model. We also empirically demonstrate the performance of the proposed approaches on several datasets.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1811.00928")
get_code_for_paper("1811.00928")
have("1811.00928")
Connect an agent — have() is free.