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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Joint Feature Selection and Structure Preservation for Domain Adaptation
Authors: Jingjing Li, Jidong Zhao, Ke Lu
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Intensive experiments on text categorization, image classification and video event recognition demonstrate that our method performs better, even with up to 30% improvement in average, compared with the stateof-the-art methods. |
| Researcher Affiliation | Academia | Jingjing Li, Jidong Zhao and Ke Lu University of Electronic Science and Technology of China, Chengdu, China |
| Pseudocode | Yes | Algorithm 1. Joint Feature Selection and Structure Preservation for Unsupervised Domain Adaptation |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | Amazon, Caltech-256, DSLR, and Webcam (4 datasets Domain Adaptation, 4DA) is the most popular benchmark in the field of domain adaptation. ... 4DA contains an additional domain, Caltech-256 (C) [Griffin et al., 2007]... Reuters-215782 is a challenging text dataset... As suggested in [Ding et al., 2015], we evaluate our approach on the preprocessed version of this dataset with the same settings of [Gao et al., 2008]. MRSC+VOC consists of two different datasets: MRSC and VOC2007. ... The large scale Columbia Consumer Video dataset (CCV) [Jiang et al., 2011] contains 9,317 web videos... |
| Dataset Splits | No | The paper describes the use of source and target domains for training and testing, but it does not specify explicit training/validation/test splits with percentages, absolute sample counts, or explicit mention of a validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software components or libraries used. |
| Experiment Setup | Yes | For consistency, we choose a common set of hyper-parameter settings for our FSSP on different evaluations. Specifically, we empirically set λ = 0.1, β = 0.1 and γ = 1. The dimensionality of subspace is set to 30, and the number of neighbors is set to 5. |