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..
Joint Dimensionality Reduction and Metric Learning: A Geometric Take
Authors: Mehrtash Harandi, Mathieu Salzmann, Richard Hartley
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments evidence that, while we directly work on high-dimensional features, our approach yields competitive runtimes with and higher accuracy than state-of-the-art metric learning algorithms. ... 6. Experimental Evaluation We now evaluate our algorithms (DRML and k DRML) and compare them with the representative baseline metric learning methods discussed above, i.e., NCA (Goldberger et al., 2004) LMNN (Weinberger & Saul, 2009), ITML (Davis et al., 2007), LDML (Guillaumin et al., 2009), KISSME (Koestinger et al., 2012) and GMML (Zadeh et al., 2016), as well as with dataset-specific baselines mentioned below. |
| Researcher Affiliation | Academia | 1Data61, CSIRO, Canberra, Australia 2Australian National University, Canberra, Australia 3CVLab, EPFL, Switzerland. |
| Pseudocode | No | The paper describes the optimization steps but does not include a structured pseudocode block or an algorithm formally labeled as such. |
| Open Source Code | Yes | In our experiments, we employed Conjugate Gradient descent on M to solve (12). In particular, we implemented the operations required for our manifold within the manopt Riemannian optimization toolbox (Boumal et al., 2014). The code is available at https://sites.google.com/site/mehrtashharandi/. |
| Open Datasets | Yes | ASLAN dataset (Kliper Gross et al., 2012) ... i LIDS dataset (Zheng et al., 2009) ... Youtube Faces (YTF) dataset (Wolf et al., 2011) |
| Dataset Splits | No | For the ASLAN dataset, the paper mentions '5,400 training and 600 testing pairs'. For the i LIDS dataset, it states 'randomly divided into two subsets, training and test'. For the YTF dataset, it mentions '10 folds' for video pairs. None of these explicitly define a separate 'validation' split. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'the manopt Riemannian optimization toolbox (Boumal et al., 2014)' but does not specify a version number for it or any other software dependencies. |
| Experiment Setup | Yes | In all our experiments, we followed the so-called restricted protocol. That is, the only information accessible to the algorithms is the similarity/dissimilarity labels of pairs of samples; the class labels of the samples are unknown. For all the methods, we report the results obtained with the best subspace dimension. ... For k DRML, we used an RBF Gaussian kernel whose bandwidth was set using Jaakkola s heuristic (Jaakkola et al., 1999). |