Manifold Spanning Graphs
Authors: CJ Carey, Sridhar Mahadevan
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The paper includes sections like 'Experimental Results', conducts evaluations on 'Parametric Swiss Roll' and 'MNIST digit clustering' datasets, and presents performance metrics in figures and tables (e.g., 'Figure 6: Summarized performance over 200 random swiss rolls', 'Table 1: Confusion matrices from the MNIST digit classification task.'). |
| Researcher Affiliation | Academia | CJ Carey and Sridhar Mahadevan School of Computer Science University of Massachusetts, Amherst Amherst, Massachusetts, 01003 {ccarey,mahadeva}@cs.umass.edu |
| Pseudocode | No | The paper describes the algorithm's stages in prose (e.g., 'The high-level procedure can be divided into three stages: 1. Divide X into many small connected components...'), but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | Source code will be made available publicly on the author s website following publication. |
| Open Datasets | Yes | We consider the classic swiss roll dataset, shown in Figure 1. the MNIST dataset of 10, 000 handwritten digits (Le Cun and Cortes 1998) |
| Dataset Splits | No | The paper states it used '200 randomly-generated swiss rolls' and for MNIST, '30 unique images were selected at random from the 10, 000-image corpus to act as labeled examples' but does not specify explicit train/validation/test splits with percentages or counts for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper states the algorithm was 'implemented in Python using open-source libraries (Oliphant 2007; Pedregosa et al. 2011)' but does not specify version numbers for Python or any of the libraries. |
| Experiment Setup | Yes | Inputs are X, an N D matrix of D-dimension points, d, an estimate of the intrinsic dimension of the underlying manifold, and m, the desired number of connected components in the final graph. Setting h d + 1 is a reasonable default, so this extra parameter requires no expert tuning. (Figure 7c caption) MSG: 1118/23042 4.9% bad edges (m = 10, d = 2) |