Dimensionality Reduction for the Sum-of-Distances Metric
Authors: Zhili Feng, Praneeth Kacham, David Woodruff
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments to empirically verify that we can attain a non-trivial amount of data reduction while still being able to compute an approximate sum of distances to a kdimensional shape. In our experiments, we set n = 10000 and k = 5. We use various subspaces to compute an approximation to the sum of distances to a k center set. |
| Researcher Affiliation | Academia | 1 Carnegie Mellon University, Pittsburgh, USA. |
| Pseudocode | Yes | Algorithm 1 POLYAPPROX; Algorithm 2 EPSAPPROX; Algorithm 3 DIMENSIONREDUCTION; Algorithm 4 COMPLETEDIMREDUCE |
| Open Source Code | Yes | An implementation of our Algorithm 3 and code for our experiments is available at here2. https://gitlab.com/praneeth10/ dimensionality-reduction-for-sum-of-distances |
| Open Datasets | Yes | We run our dimensionality reduction algorithm on a randomly chosen subset A of size 10000 of the Cover Type dataset (Dua and Graff, 2017). URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | No | The paper mentions using a subset of the Cover Type dataset but does not specify any training, validation, or test splits, or the use of cross-validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments (e.g., libraries, frameworks). |
| Experiment Setup | No | The paper mentions setting a "target dimension of 100" in the experiments, but it does not provide comprehensive details on other experimental setup parameters like learning rates, batch sizes, optimizers, or training schedules. |