Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering
Authors: Shyam Narayanan, Sandeep Silwal, Piotr Indyk, Or Zamir
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we provide experimental results to validate the quality of solutions and the speedup due to the dimensionality reduction. ... Finally, we present an experimental evaluation of the algorithms suggested by our results. |
| Researcher Affiliation | Academia | 1Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA, USA 2Institute of Advanced Study, Princeton, NJ, USA. |
| Pseudocode | No | The paper refers to existing algorithms like the Mettu Plaxton (MP) algorithm and Boruvka algorithm but does not include any pseudocode or algorithm blocks for its own methods. |
| Open Source Code | No | The paper mentions the use of the 'mlpack machine learning library' (Curtin et al., 2018) but does not provide any statement or link indicating that its own methodology's source code is available. |
| Open Datasets | Yes | Faces Dataset: This dataset is used in the influential ISOMAP paper and consists of 698 images of faces in dimension 4096 (Tenenbaum et al., 2000). ... MNIST 2 Dataset: 1000 randomly chosen images from the MNIST dataset (dimension 784) restricted to the digit 2. We picked 2 since it is considered in the original ISOMAP paper (Tenenbaum et al., 2000). |
| Dataset Splits | No | The paper mentions using datasets for experiments but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or explicit standard split references). |
| Hardware Specification | Yes | All of our experiments were done on a CPU with i5 2.7 GHz dual core and 8 GB RAM. |
| Software Dependencies | No | The paper mentions the use of 'mlpack machine learning library' but does not provide a specific version number for it or other software dependencies. |
| Experiment Setup | Yes | We project our datasets and compute a facility location clustering with the opening costs scaled so that n/2, n/5, and n/10 facilities are opened respectively. ... All of our experimental results are averaged over 20 independent trials and the projection dimension d ranges from 5 to 20 inclusive. |