Multilevel Clustering via Wasserstein Means
Authors: Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Phung
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experiment results with both synthetic and real data are presented to demonstrate the flexibility and scalability of the proposed approach. |
| Researcher Affiliation | Collaboration | 1Department of Statistics, University of Michigan, USA. 2Adobe Research. 3Center for Pattern Recognition and Data Analytics (PRa DA), Deakin University, Australia. |
| Pseudocode | Yes | Algorithm 1 Multilevel Wasserstein Means (MWM) |
| Open Source Code | Yes | 1Code is available at https://github.com/ moonfolk/Multilevel-Wasserstein-Means |
| Open Datasets | Yes | Label Me dataset consists of 2, 688 annotated images which are classified into 8 scene categories including tall buildings, inside city, street, highway, coast, open country, mountain, and forest (Oliva and Torralba, 2001). |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or testing splits for the datasets used in experiments. |
| Hardware Specification | Yes | This implementation has the advantage of making use of all of 16 cores on the test machine. |
| Software Dependencies | No | The paper mentions 'Spark-based implementation' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Unless otherwise specified, we set the number of groups m = 50, number of observations per group nj = 50 in d = 10 dimensions, number of global clusters M = 5 with 6 atoms. For Algorithm 1 (MWM) local measures Gj have 5 atoms each; for Algorithm 2 (MWMS) number of atoms in constraint set SK is 50. |