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..
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces
Authors: Minh Ha Quang, Marco San Biagio, Vittorio Murino
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we apply our formulation to the task of multi-category image classification, where each image is represented by an infinite-dimensional RKHS covariance operator. On several challenging datasets, our method significantly outperforms approaches based on covariance matrices computed directly on the original input features, including those using the Log-Euclidean metric, Stein and Jeffreys divergences, achieving new state of the art results. |
| Researcher Affiliation | Academia | H a Quang Minh Marco San Biagio Vittorio Murino Istituto Italiano di Tecnologia Via Morego 30, Genova 16163, ITALY EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | Kylberg texture dataset [13], KTH-TIPS2b dataset [6], Fish Recognition dataset [5] |
| Dataset Splits | Yes | For all experiments, the kernel parameters were chosen by cross validation, while the regularization parameters were fixed to be γ = µ = 10 8. We randomly selected 5 images in each class for training and used the remaining ones as test data, repeating the entire procedure 10 times. |
| 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 using LIBSVM [7] but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For all experiments, the kernel parameters were chosen by cross validation, while the regularization parameters were fixed to be γ = µ = 10 8. |