Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
Authors: Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing, Maneesh Sahani
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the approach on several real world problems. ... 4 Experiments 4.1 Mitigating the short-circuit problem ... 4.2 Modelling USPS handwritten digits ... 4.3 Mapping climate data |
| Researcher Affiliation | Collaboration | Gatsby Computational Neuroscience Unit University College London {mijung, wittawat, zoltan.szabo}@gatsby.ucl.ac.uk atqamar@gmail.com, lbuesing@google.com, maneesh@gatsby.ucl.ac.uk Current affiliation: Thread Genius Current affiliation: Google Deep Mind |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | An implementation is available from http://www.gatsby.ucl.ac.uk/resources/lllvm. |
| Open Datasets | Yes | We test our method on a subset of 80 samples each of the digits 0, 1, 2, 3, 4 from the USPS digit dataset... Data were obtained by averaging month-by-month annual precipitation records from 2005 2014 at 400 weather stations scattered across the US (see Fig. 6)... The dataset is made available by the National Climatic Data Center at http://www.ncdc.noaa. gov/oa/climate/research/ushcn/. We use version 2.5 monthly data [15]. |
| Dataset Splits | Yes | Using the extracted features (in 2D), we evaluated a 1-NN classifier for digit identity with 10-fold cross-validation (the same data divided into 10 training and test sets). |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory, or cluster specifications) used for experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | Yes | The full EM algorithm starts with an initial value of θ. ... The two steps are repeated until the variational lower bound in Eq. (6) saturates. ... using 9 different EM initialisations. ... For the graph-based methods LL-LVM, LTSA, ISOMAP, and LLE, we used 12-NN with Euclidean distance to construct the neighbourhood graph. |