Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data
Authors: Yubo Zhuang, Xiaohui Chen, Yun Yang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numeric experiments demonstrate that i LASDP can achieve lower mis-clustering errors over several widely used clustering methods including K-means, SDP and EM algorithms. In this section, we compare the performance of several widely used clustering methods on two real datasets. |
| Researcher Affiliation | Academia | 1Department of Statistics, University of Illinois at Urbana Champaign. 2Department of Mathematics, University of Southern California. Correspondence to: Yubo Zhuang <yubo2@illinois.edu>, Xiaohui Chen <xiaohuic@usc.edu>, Yun Yang <yy84@illinois.edu>. |
| Pseudocode | Yes | Algorithm 1 The iterative likelihood adjusted SDP (i LASDP) algorithm |
| Open Source Code | No | The paper does not contain any explicit statement or link providing access to the source code for the methodology described. |
| Open Datasets | Yes | The first one is handwriting digits dataset MNIST, the second one is CIFAR-10 image dataset and the last one is Landsat dataset from the UCI machine learning repository. |
| Dataset Splits | No | The paper mentions the use of a "test set" for various datasets (MNIST, CIFAR-10, Landsat) but does not provide explicit details about training or validation splits (e.g., percentages or sample counts for all splits), only the size of the test sets. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions "hierarchical clustering from mclust package in R" but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | For all the dimension reduction procedures used in the simulation experiments, we perform step 1-7 in Algorithm 2 followed by Algorithm 3 with input parameters α = 0.7, C = 1010, p0 = 2K, p1 = 15 ϵ = 10 2, S = 50. |