Ultrametric Fitting by Gradient Descent
Authors: Giovanni Chierchia, Benjamin Perret
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed cost functions on synthetic and real datasets, and we show that they perform as good as Ward method and semi-supervised SVM. |
| Researcher Affiliation | Academia | Giovanni Chierchia Université Paris-Est, LIGM (UMR 8049) CNRS, ENPC, ESIEE Paris, UPEM F-93162, Noisy-le-Grand, France giovanni.chierchia@esiee.fr |
| Pseudocode | Yes | Algorithm 1 Solution to the ultrametric fitting problem defined in (4). Algorithm 2 Subdominant ultrametric operator defined in (5) with (14). |
| Open Source Code | Yes | Our code is made publicly available at https://github.com/Perret B/ultrametric-fitting. |
| Open Datasets | Yes | We evaluate the proposed optimization framework on five datasets downloaded from the LIBSVM webpage,3 whose size ranges from 270 to 1500 samples. For each dataset, we build a 5-nearest-neighbor graph, to which we add the edges of a minimum spanning tree to ensure the connectivity. https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper mentions a "10-fold scheme" for cross-validation and that "cross-validated performance is reported," but it does not specify explicit training/validation/test dataset splits where a separate validation set is used for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Higra [44] and Py Torch [45] libraries" as dependencies but does not specify their version numbers. |
| Experiment Setup | Yes | The "Dasgupta" method refers to Algorithm 1 with JDasgupta + λJsize and λ = 1. The "Closest+Size" method refers to Algorithm 1 with the cost function Jclosest + λJsize and λ = 10. In both cases, the regularization is only applied to the top-10 dendogram nodes (see supplemental material). The "Closest+Triplet" method refers to Algorithm 1 with Jclosest + λJtriplet, λ = 1 and α = 10. |