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..
Solving Interpretable Kernel Dimensionality Reduction
Authors: Chieh Wu, Jared Miller, Yale Chang, Mario Sznaier, Jennifer Dy
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments. The experiment includes 5 real datasets of commonly encountered data types. Wine [33] consists of continuous data while the Cancer dataset [34] features are discrete. The Face dataset [35] is a standard dataset used for alternative clustering; it includes images of 20 people in various poses. The MNIST [36] dataset includes images of handwritten characters. |
| Researcher Affiliation | Academia | Electrical and Computer Engineering Dept., Northeastern University, Boston, MA |
| Pseudocode | Yes | Algorithm 1 ISM Algorithm Input : Data X, kernel, Subspace Dimension q Output : Projected subspace W |
| Open Source Code | Yes | To support reproducible results, the source code is made publicly available on https://github.com/chieh-neu/ISM_supervised_DR. |
| Open Datasets | Yes | Wine [33] consists of continuous data while the Cancer dataset [34] features are discrete. The Face dataset [35] is a standard dataset used for alternative clustering; it includes images of 20 people in various poses. The MNIST [36] dataset includes images of handwritten characters. |
| Dataset Splits | Yes | For supervised dimension reduction, we perform SVM on XW using 10-fold cross validation. |
| Hardware Specification | Yes | All experiments were conducted on Dual Intel Xeon E5-2680 v2 @ 2.80GHz, with 20 total cores. |
| Software Dependencies | No | All sources are written in Python using Numpy and Sklearn [41; 42]. Specific version numbers for Python, Numpy, or Sklearn are not provided. |
| Experiment Setup | Yes | The median of the pair-wise Euclidean distance is used as σ for all experiments using the Gaussian kernel. Degree of 3 is used for all polynomial kernels. The dimension of subspace q is set to the number of classes/clusters. The convergence threshold δ is set to 0.01. |