Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
Authors: Tam Le, Makoto Yamada
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Throughout experiments with many different tasks on various benchmark datasets, we illustrate that the PF kernel compares favorably with other baseline kernels for PDs. |
| Researcher Affiliation | Academia | Tam Le RIKEN Center for Advanced Intelligence Project, Japan EMAIL Makoto Yamada Kyoto University, Japan RIKEN Center for Advanced Intelligence Project, Japan EMAIL |
| Pseudocode | Yes | Algorithm 1 Compute d FIM for persistence diagrams |
| Open Source Code | Yes | Source code for Algorithm 1 can be obtained in http://github.com/lttam/Persistence Fisher. |
| Open Datasets | Yes | It is a synthesized dataset proposed by [Adams et al., 2017] ( 6.4.1) for linked twist map... We consider a 10-class subset7 of MPEG7 object shape dataset [Latecki et al., 2000]. ... granular packing system [Francois et al., 2013] and Si O2 [Nakamura et al., 2015] datasets. |
| Dataset Splits | No | The paper mentions training and testing splits, but does not explicitly describe a validation set split. For example, it states "We randomly split 70%/30% for training and test, and repeated 100 times." |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments, such as GPU/CPU models or memory amounts. |
| Software Dependencies | No | The paper mentions "Libsvm (one-vs-one) [Chang and Lin, 2011]" and "the DIPHA toolbox6" but does not specify their version numbers for reproducibility. |
| Experiment Setup | Yes | For hyper-parameters, we typically choose them through cross validation. For baseline kernels, we follow their corresponding authors to form sets of hyper-parameter candidates, and the bandwidth of the Gaussian kernel in (Prob + k G) and (Tang + k G) is chosen from 10{ 3:1:3}. For the Persistence Fisher kernel, there are 2 hyper-parameters: t (Equation (4)) and σ for smoothing measures (Equation (1)). We choose 1/t from {q1, q2, q5, q10, q20, q50} where qs is the s% quantile of a subset of Fisher information metric between PDs, observed on the training set, and σ from 10 3:1:3 . For SVM, we use Libsvm (one-vs-one) [Chang and Lin, 2011] for multi-class classification, and choose a regularization parameter of SVM from 10 2:1:2 . |