Ratio Trace Formulation of Wasserstein Discriminant Analysis
Authors: Hexuan Liu, Yunfeng Cai, You-Lin Chen, Ping Li
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on real datasets show promising results of the ratio trace formulation of WDA in both classification and clustering tasks. |
| Researcher Affiliation | Industry | Hexuan Liu, Yunfeng Cai, You-Lin Chen, Ping Li Cognitive Computing Lab Baidu Research No.10 Xibeiwang East Road, Beijing 100193, China 10900 NE 8th St. Bellevue, Washington 98004, USA {lhxuan93, yunfengcai09, cyoulin.tw, pingli98}@gmail.com |
| Pseudocode | Yes | Algorithm 1 WDA-eig algorithm Algorithm 2 Iterative WDA clustering |
| Open Source Code | No | The paper does not contain an explicit statement that the authors' code for the described methodology is open-source, nor does it provide a direct link to a code repository for their implementation. |
| Open Datasets | Yes | We extract 1000 samples in the MNIST dataset as the training set and use 10000 samples in the test set. We use four real world datasets to evaluate the proposed method: the MNIST dataset for digits recognition, the 15-scene dataset [19] for multi-class image recognition, the KTH action recognition database [33] for multi-class video recognition, and the 20 newsgroup dataset for text classification. |
| Dataset Splits | No | The paper specifies a training and test set split for MNIST but does not explicitly mention a separate validation set or its split percentage for any dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. It only mentions experiments were run and evaluated. |
| Software Dependencies | No | The paper mentions software like "pymanopt solvers", "Sinkhorn’s fixed-point iterations", "Greenkhorn algorithm", "APDAMD", "K-Nearest-Neighbors classifier (KNN)", "K-means", "FDA", "PCA", "LFDA" but does not specify version numbers for these components, which would be necessary for reproducible software dependencies. |
| Experiment Setup | Yes | For each λ, we run each algorithms for 100 randomly-initialized trials, and the results are shown in Table 1. In implementation of FDA/WDA-eig we add a small perturbation term ϵIp on Cw to make the denominator positive definite, and we choose ϵ = 2 in this setting... The Wasserstein regularizer λ is coarsely tuned, where we choose λ = 0.01 for MNIST and 15-scene, λ = 10 for KTH, and λ = 5 for 20ng. For (3) and (4) we use the subspace obtained by PCA as initialization and p = Nc 1 as the subspace dimensions. No regularization term is added to Cw. |