Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Latent Discriminant Analysis with Representative Feature Discovery
Authors: Gang Chen
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method on MUSK and Corel datasets and yield competitive results compared to baselines. We also demonstrate its capacity on the challenging TRECVID MED11 dataset for semantic keyframe extraction and conduct a human-factors ranking-based experimental evaluation, which clearly demonstrates our proposed method consistently extracts more semantically meaningful keyframes than challenging baselines. |
| Researcher Affiliation | Academia | Gang Chen Department of Computer Science and Engineer SUNY at Buffalo, Buffalo, NY 14260 EMAIL |
| Pseudocode | Yes | Algorithm 1 Input: training data X and its labels L at video level, β, K, N, T and ϵ. Output: P, {λi}C i=1... |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing its own source code, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | MUSK data sets1 are the benchmark data sets used in virtually all previous approaches and have been described in detail in the landmark paper (Dietterich, Lathrop, and Lozano-P erez 1997).1 www.cs.columbia.edu/ andrews/mil/datasets.html ... We conduct experiments on the challenging TRECVID MED11 dataset3 ... 3 http://www.nist.gov/itl/iad/mig/med11.cfm |
| Dataset Splits | Yes | The averaged results of 10-fold cross-validation runs are summarized in Table (1). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using "SVM light" and "Matlab" but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For all the experiments, we set T = 20 and β = 40 if there is no other specification; and initialize uniformly weighted wi and projection matrix P with LDA. ... For parameter setting, we set K=3, T = 20 and N = 4 (namely the 4-Nearest Neighbor (4NN) algorithm is applied for classification) on all datasets except Elephant (we set K = 2 for it). |