Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Authors: Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek
JMLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further show experimental results on several different types of multi-view data sets and for different kinds of tasks, including exploratory data analysis, generation, ambiguity modelling through latent priors and classification. |
| Researcher Affiliation | Collaboration | Andreas Damianou EMAIL Amazon, Cambridge, United Kingdom Neil D. Lawrence EMAIL University of Cambridge, United Kingdom Carl Henrik Ek EMAIL University of Cambridge, United Kingdom |
| Pseudocode | Yes | Algorithm 1 Inference algorithm in MRD, assuming two sets of views YA and YB |
| Open Source Code | No | The text does not contain a specific link to source code or an explicit statement about its public release. The URL provided (http://git.io/vw Lh H) points to online videos, not code. |
| Open Datasets | Yes | The paper uses several publicly available datasets, each with proper citation: 'Yale face database B (Georghiades et al., 2001)', 'data set of Agarwal and Triggs (2006)', 'oil flow database (Bishop and James, 1993)', and 'AVletters database (Matthews et al., 2002)'. |
| Dataset Splits | Yes | For the Pose Estimation experiment: 'We used a subset of 5 sequences, totaling 649 frames... A separate walking sequence of 158 frames was used as a test set.' For AVletters: 'letters B , M and T were left out of the training set completely to be used at test time. For each modality, we thus had 69 rows (23 letters * 3 trials)... In the test set, each modality had only 9 rows (3 letters * 3 trials).' Table 2 further details the view/row split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their respective versions) used for implementation or experimentation. |
| Experiment Setup | Yes | The paper specifies experimental setup details such as the initialization of latent dimensions (q). For example, 'The model is initialized with q = 8 latent dimensions' for toy data, 'q = 14 latent dimensions' for Yale Faces, and 'q = 15 latent dimensions' for Pose Estimation. It also mentions 'In our experiments we use ε = 10^-3' for the threshold value in latent space segmentation. |