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 [1].
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. |