M Saquib Sarfraz, Marios Koulakis, Rainer Stiefelhagen, Constantin Seibold
We lifted 19 functions out of this paper's own repositories and ran 13 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 |
|---|---|---|
| koulakis/h-nne | canonical | 13 of 19 |
| Function | Status | Where it lives |
|---|---|---|
| cool_max | Ran | koulakis/h-nne/hnne/cool_functions.py code served (permissive licence) · get_code("41c5cbd17860e340") |
| cool_max_radius | Ran | koulakis/h-nne/hnne/cool_functions.py code served (permissive licence) · get_code("aebb70457b42c029") |
| cool_mean | Ran | koulakis/h-nne/hnne/cool_functions.py code served (permissive licence) · get_code("837397a9051a72c6") |
| format_count | Ran | koulakis/h-nne/benchmarking/hnne_benchmarking/utils.py code served (permissive licence) · get_code("d6a85b26b54e1f6a") |
| format_metric | Ran | koulakis/h-nne/benchmarking/hnne_benchmarking/evaluation.py code served (permissive licence) · get_code("3b8cd9eb4ca50ffa") |
| format_time | Ran | koulakis/h-nne/benchmarking/hnne_benchmarking/utils.py code served (permissive licence) · get_code("8997d3690a281c6b") |
| layout_to_level_arrays | Ran | koulakis/h-nne/hnne/v2/v2_utils.py code served (permissive licence) · get_code("0865915de00bb565") |
| load_coil20 | Ran | koulakis/h-nne/benchmarking/hnne_benchmarking/data.py code served (permissive licence) · get_code("2bfc48e152c98698") |
| normalize_hnne_version | Ran | koulakis/h-nne/hnne/v2/v2_utils.py code served (permissive licence) · get_code("0a2fb4a937a86f55") |
| plot_projection_grid | Ran | koulakis/h-nne/benchmarking/hnne_benchmarking/v2_plotter.py code served (permissive licence) · get_code("ba82bc6d5863b5e3") |
| project_with_pca | Ran | koulakis/h-nne/hnne/v1/hierarchical_projection.py code served (permissive licence) · get_code("08504cde0793e9d9") |
| rescale_layout | Ran | koulakis/h-nne/hnne/v2/v2_utils.py code served (permissive licence) · get_code("201dba0c54368168") |
| separate_overlaps_minmove_grid_safe_nd | Ran | koulakis/h-nne/hnne/v2/v2_packer.py code served (permissive licence) · get_code("8e6b2090f7f06bda") |
| load_mnist | Not yet run | koulakis/h-nne/benchmarking/hnne_benchmarking/data.py code served (permissive licence) · get_code("36476255697ea82f") |
| load_shuttle | Not yet run | koulakis/h-nne/benchmarking/hnne_benchmarking/data.py code served (permissive licence) · get_code("2432b35641ba5335") |
| pack_circles_from_anchors_2d_spacefill_k | Not yet run | koulakis/h-nne/hnne/v2/v2_packer.py code served (permissive licence) · get_code("3d0a9711c999f375") |
| plot_v2_nested_2d | Not yet run | koulakis/h-nne/benchmarking/hnne_benchmarking/v2_plotter.py code served (permissive licence) · get_code("b88f471934cdcb53") |
| separate_overlaps_minmove_grid_safe_2d | Not yet run | koulakis/h-nne/hnne/v2/v2_packer.py code served (permissive licence) · get_code("f40a4e1b0adc14a5") |
| time_function_call | Not yet run | koulakis/h-nne/benchmarking/hnne_benchmarking/utils.py code served (permissive licence) · get_code("903bbe6e8ca73ca2") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Dimensionality reduction is crucial both for visualization and preprocessing high dimensional data for machine learning. We introduce a novel method based on a hierarchy built on 1-nearest neighbor graphs in the original space which is used to preserve the grouping properties of the data distribution on multiple levels. The core of the proposal is an optimization-free projection that is competitive with the latest versions of t-SNE and UMAP in performance and visualization quality while being an order of magnitude faster at run-time. Furthermore, its interpretable mechanics, the ability to project new data, and the natural separation of data clusters in visualizations make it a general purpose unsupervised dimension reduction technique. In the paper, we argue about the soundness of the proposed method and evaluate it on a diverse collection of datasets with sizes varying from 1K to 11M samples and dimensions from 28 to 16K. We perform comparisons with other state-of-the-art methods on multiple metrics and target dimensions highlighting its efficiency and performance. Code is available at https://github.com/koulakis/h-nne
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2203.12997")
get_code_for_paper("2203.12997")
have("2203.12997")
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