Probabilistic Partial Canonical Correlation Analysis
Authors: Yusuke Mukuta, Harada
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments demonstrated that our methods can stably estimate the model parameters, even in high dimensions or when there are a small number of samples. |
| Researcher Affiliation | Academia | Yusuke Mukuta MUKUTA@MI.T.U-TOKYO.AC.JP Graduate School of Information Science and Technology, The University of Tokyo 7 3 1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan Tatsuya Harada HARADA@MI.T.U-TOKYO.AC.JP Graduate School of Information Science and Technology, The University of Tokyo 7 3 1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan |
| Pseudocode | No | The paper describes methods through mathematical formulations and text but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | Next, we applied GSPCCA and PCCA to meteorological data, using the Global Summary of the Day (GSOD) provided by the National Climatic Data Center (NCDC) on its website. |
| Dataset Splits | No | The paper mentions 'five-fold cross validation (CV)' as a model selection technique, but does not explicitly provide the training, validation, or test dataset splits needed to reproduce the main experiments. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or specialized solvers. |
| Experiment Setup | Yes | In our experiments, we set a0, b0 = 10 14, νm 0 = dm, Km 0 = 10 14 Idm. The ARD prior drives unnecessary components to zero, so we can estimate the dimensions of the latent variables by choosing sufficiently large dz, or by first choosing a small dz and then gradually increasing it according to the output projection matrices. We refer to this model as Bayesian PCCA (BPCCA). |