Local Context Sparse Coding
Authors: Seungyeon Kim, Joonseok Lee, Guy Lebanon, Haesun Park
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 ExperimentsWe examine in this section using features generated by LCSC in classification. We used a standard classifier, support vector machine1, with different sets of features. Specifically, we used ν-SVM whose ν value was selected from 10 candidate values using cross-validation. Our classification task was the standard 20 newsgroup2 classification data with the official train-test split and standard preprocessing |
| Researcher Affiliation | Collaboration | College of Computing, Georgia Institute of Technology, Atlanta, GA, USA Amazon, Seattle, WA, USA |
| Pseudocode | Yes | Algorithm 1 Greedy coordinate descent for β and z |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the Local Context Sparse Coding (LCSC) methodology described. |
| Open Datasets | Yes | Our classification task was the standard 20 newsgroup2 classification data with the official train-test split and standard preprocessing: lowercasing, stripping non-english characters, tokenizing sentences and words, Porter stemming, and removing rare features and stop words. (Footnote 2 points to http://qwone.com/~jason/20Newsgroups/) |
| Dataset Splits | Yes | Specifically, we used ν-SVM whose ν value was selected from 10 candidate values using cross-validation. Our classification task was the standard 20 newsgroup2 classification data with the official train-test split and standard preprocessing |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper mentions using 'support vector machine' (referencing LibSVM), 'Fast LDA', and 'STC' but does not provide specific version numbers for these software dependencies or any other ancillary software. |
| Experiment Setup | Yes | Specifically, we used ν-SVM whose ν value was selected from 10 candidate values using cross-validation. Our classification task was the standard 20 newsgroup2 classification data with the official train-test split and standard preprocessing: lowercasing, stripping non-english characters, tokenizing sentences and words, Porter stemming, and removing rare features and stop words. ... The bandwidth of LCSC was fixed to h=1, which covers about 7 words ( 3h). We tried a set of candidates for the remaining parameters and chose the best performing one (for example, λ = {10 4, 10 2, 10 1, 0.5, 1} for STC). ... Figure 5 shows test set classification accuracies of LCSC with various bandwidths while other parameters are fixed (K=4000, ρ=10 4, λ=10 2). |