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..
Efficient Structure-preserving Support Tensor Train Machine
Authors: Kirandeep Kour, Sergey Dolgov, Martin Stoll, Peter Benner
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4 we benchmark the different steps of the proposed algorithm and compare it to a variety of competing methods using two data sets each from two different fields with a limited amount of training data, which are known to be challenging for classification. Experimental Settings All numerical experiments have been done in MATLAB 2016b. Table 2: Average classification accuracy in percentage for different methods and data sets |
| Researcher Affiliation | Academia | Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg, D-39106, Germany. Department of Mathematical Sciences University of Bath Bath BA2 7AY, United Kingdom. Faculty of Mathematics Technische Universität Chemnitz Chemnitz, D-09107, Germany. |
| Pseudocode | Yes | Algorithm 1: Uniqueness Enforcing TT-SVD. Algorithm 2: TT-CP approximation of the STM Kernel |
| Open Source Code | Yes | The codes are available publicly on Git Hub3. 3. https://github.com/mpimd-csc/Structure-preserving_STTM |
| Open Datasets | Yes | Alzheimer Disease (ADNI): The ADNI4 stands for Alzheimer Disease Neuroimaging Initiative. ... 4. http://adni.loni.usc.edu/. Attention Deficit Hyperactivity Disorder (ADHD): The ADHD data set is collected from the ADHD-200 global competition data set5. ... 5. http://neurobureau.projects.nitrc.org/ADHD200/Data.html. Hyperspectral Image (HSI) Datasets: We have taken the mat file for both the datasets and their corresponding labels6. ... 6. http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes. The HSI images were collected via the Aviris Sensor7 over the Indian Pines test site. ... 7. https://aviris.jpl.nasa.gov/ |
| Dataset Splits | Yes | Since the precise magnitude of the noise is unknown, we carry out a k-fold cross-validation test (k = 5) to find the optimal TT rank. For tuning R, σ and C to the best classification accuracy, we use the k-fold cross validation with k = 5. |
| Hardware Specification | Yes | We have run all experiments on a machine equipped with Ubuntu release 16.04.6 LTS 64-bit, 7.7 Gi B of memory, and an Intel Core i5-6600 CPU @ 3.30GHz 4 CPU. |
| Software Dependencies | Yes | All numerical experiments have been done in MATLAB 2016b. In the first step, we compute the TT format of an input tensor using the TT-Toolbox1, where we modified the function @tt_tensor/round.m to enforce the uniqueness enforcing TT-SVD (Section 3.1). Moreover, we have implemented the TT-CP conversion, together with the norm equilibration. For the training of the TT-MMK model, we have used the svmtrain function available in the LIBSVM2 library. |
| Experiment Setup | Yes | The entire TT-SVM model depends on three parameters. First, to simplify the selection of TT ranks, we take all TT ranks equal to the same value R {1, 2, . . . 10}. Another parameter is the width of the Gaussian Kernel σ. Finally, the third parameter is a trade-off constant C for the KSTM optimization technique (6). Both σ and C are chosen from {2 8, 2 7, . . . , 27, 28}. For tuning R, σ and C to the best classification accuracy, we use the k-fold cross validation with k = 5. Along with this, we repeat all computations 20 times and compute statistics (average, standard deviation, and numerical quantiles) over these runs. |