We lifted 2 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| copy not recorded | — | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| calcCostMatrix | Ran | this paper's copy was not recorded; identical code first harvested from ofirlin/dufs pointer only · get_code("df0929bf094467b6") |
| getClusterLabelsFromIndexes | Ran | this paper's copy was not recorded; identical code first harvested from ofirlin/dufs pointer only · get_code("c656994e81f38a99") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Scientific observations may consist of a large number of variables (features). Identifying a subset of meaningful features is often ignored in unsupervised learning, despite its potential for unraveling clear patterns hidden in the ambient space. In this paper, we present a method for unsupervised feature selection, and we demonstrate its use for the task of clustering. We propose a differentiable loss function that combines the Laplacian score, which favors low-frequency features, with a gating mechanism for feature selection. We improve the Laplacian score, by replacing it with a gated variant computed on a subset of features. This subset is obtained using a continuous approximation of Bernoulli variables whose parameters are trained to gate the full feature space. We mathematically motivate the proposed approach and demonstrate that in the high noise regime, it is crucial to compute the Laplacian on the gated inputs, rather than on the full feature set. Experimental demonstration of the efficacy of the proposed approach and its advantage over current baselines is provided using several real-world examples.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2007.04728")
get_code_for_paper("2007.04728")
have("2007.04728")
Connect an agent — have() is free.