The Variational Nystrom method for large-scale spectral problems
Authors: Max Vladymyrov, Miguel Carreira-Perpinan
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experiments To set up the spectral problem (P) we want to approximate, we use Laplacian Eigenmaps (LE) (Belkin & Niyogi, 2003), a spectral manifold learning algorithm. We also use spectral clustering (SC) in an image segmentation experiment. We compare the approximations to the exact embedding X or the ground truth, when available. Fig. 1 (top) shows the relative error with respect to the ground truth as we change the Gaussian affinity bandwidth σ. |
| Researcher Affiliation | Collaboration | Max Vladymyrov MXV@GOOGLE.COM Google, Inc. Miguel A. Carreira-Perpi n an MCARREIRA-PERPINAN@UCMERCED.EDU Electrical Engineering and Computer Science, School of Engineering, University of California, Merced |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an unambiguous statement of code release or a direct link to a source-code repository for the described methodology. |
| Open Datasets | Yes | We used 5 000 points from the Swiss roll dataset, for which the ground truth is available. ... We use 20 000 random digits from MNIST and reduce dimensionality to d = 10 using exact LE and each of the methods. ... We used 1 020 000 points from the infinite MNIST dataset (Loosli et al., 2007) |
| Dataset Splits | No | The paper does not explicitly provide specific percentages, sample counts, or citations to predefined train/validation/test splits for the datasets, nor does it detail a specific splitting methodology for data partitioning. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, processor types, or memory amounts) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions 'Matlab s eigs routine' but does not specify the version number for Matlab or any other software dependencies with their specific versions. |
| Experiment Setup | Yes | To compute X, we use Matlab s eigs routine with default parameters (maxit = 300, tol = eps, p = 2d). ... We construct the affinity matrix using entropic affinities ... with perplexity K = 30 ... We also sparsify the affinity matrix by zeroing all but the 200 largest values in each row. |