Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm
Authors: Kejun Huang, Xiao Fu, Nikolaos D. Sidiropoulos
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments using the TDT2 and Reuters-21578 corpus demonstrate that the proposed anchor-free approach exhibits very favorable performance (measured using coherence, similarity count, and clustering accuracy metrics) compared to the prior art. |
| Researcher Affiliation | Academia | Kejun Huang Xiao Fu Nicholas D. Sidiropoulos Department of Electrical and Computer Engineering University of Minnesota Minneapolis, MN 55455, USA huang663@umn.edu xfu@umn.edu nikos@ece.umn.edu |
| Pseudocode | Yes | Algorithm 1: Successive Projection Algorithm [6]... Algorithm 2: Anchor Free |
| Open Source Code | No | The paper describes its algorithms and experimental procedures but does not provide a link or explicit statement about making the source code for its method publicly available. |
| Open Datasets | Yes | In this section, we apply the proposed algorithm and the baselines to two popular text mining datasets, namely, the NIST Topic Detection and Tracking (TDT2) and the Reuters-21578 corpora. |
| Dataset Splits | No | The paper mentions using TDT2 and Reuters-21578 datasets for experiments but does not specify exact training, validation, or test split percentages, sample counts, or refer to predefined splits with citations for reproducibility of data partitioning. |
| Hardware Specification | No | The paper discusses runtime performance and computational complexity but does not specify the particular hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'well-established tools for eigen-decomposition' and 'the solver proposed in [18]' but does not provide specific version numbers for any software dependencies, libraries, or programming environments. |
| Experiment Setup | No | The paper describes data preprocessing steps (e.g., 'standard tf-idf data', 'normalized-cut weighted (NCW) [21] is applied') but does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or explicit training configurations. |