Towards a Persistence Diagram that is Robust to Noise and Varied Densities
Authors: Hang Zhang, Kaifeng Zhang, Kai Ming Ting, Ye Zhu
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation reveals that the proposed filter function provides a better means for t-SNE visualization and SVM classification than three existing methods of TDA. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2Centre for Cyber Resilience and Trust, Deakin University, Burwood, VIC, Australia. |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | 1The code of Λ-filter is available at https://github.com/IsolationKernel/Codes/tree/main/Lambda-kernel |
| Open Datasets | Yes | The dataset we used consists of 150 images (or point clouds C1, ..., C150) from 3 types of cells in tumor regions (Vipond et al., 2021) |
| Dataset Splits | Yes | In each split, we take 70% of the whole dataset for training and 30% for testing. 3-fold cross-validation on the training set is used to select the best hyperparameters for each approach |
| Hardware Specification | Yes | The experiments are performed on a machine with 1500MHz CPUs and 2TB RAM. |
| Software Dependencies | No | The paper mentions software like t-SNE, SVM, and kNN classifier but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For Λ-kernel, t = 200, η = ∞, ψ is searched over {2, 4, 8, 16, 32}. For DTM and Ck NN, the k is searched in {m n|m = 0.02, 0.04, 0.06, 0.08, 0.1}, where n is the dataset size. |