Learning Robust Representations for Data Analytics
Authors: Sheng Li
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on image datasets show promising results on clustering and semisupervised classification [Li and Fu, 2015a]. These methods have obtained remarkable improvements on many real-world applications, such as image classification [Li and Fu, 2015a], human motion segmentation [Li et al., 2015a], person re-identification [Li et al., 2015b], etc.. Experimental results on three action and gesture datasets show that TSC outperforms the related methods, which validates the effectiveness of robust dictionary learning [Li et al., 2015a]. |
| Researcher Affiliation | Academia | Sheng Li Advisor: Yun Fu Northeastern University, Boston, MA, USA |
| Pseudocode | No | The paper describes algorithms (e.g., "Non-convex optimization algorithms are designed"), but it does not include pseudocode or algorithm blocks formatted as such. |
| Open Source Code | No | The paper does not provide any specific link or statement regarding the release of its source code. |
| Open Datasets | Yes | These methods have obtained remarkable improvements on many real-world applications, such as image classification [Li and Fu, 2015a], human motion segmentation [Li et al., 2015a], person re-identification [Li et al., 2015b], etc.. Experimental results on three action and gesture datasets show that TSC outperforms the related methods, which validates the effectiveness of robust dictionary learning [Li et al., 2015a]. |
| Dataset Splits | No | The paper mentions experiments on datasets but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | No | The paper provides an objective function but does not include specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |