Joint Feature Selection and Structure Preservation for Domain Adaptation
Authors: Jingjing Li, Jidong Zhao, Ke Lu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |