Structured Sparsity with Group-Graph Regularization
Authors: Xin-Yu Dai, Jian-Bing Zhang, Shu-Jian Huang, Jia-Jun Chen, Zhi-Hua Zhou
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real data show that, enforcing group-graph sparsity lead to better performance than using group sparsity or graph sparsity only. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China {daixinyu,zjb,huangsj,chenjj,zhouzh}@nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 The g2-regularization method |
| Open Source Code | No | A modified open-source software named SPAMS from http://spams-devel.gforge.inria.fr/ is used to implement our algorithm. |
| Open Datasets | Yes | MEMset Dataset (1) Experiment Setup This dataset is available at http://genes.mit.edu/burgelab/maxent/ssdata/. and NN269 Dataset (1) Experiment Setup We use the NN269 dataset for more real-world data evaluation (Reese et al. 1997), which is available at http://www.fruitfly.org/data/seqtools/datasets/Human/GENIE96/splicesets/. |
| Dataset Splits | Yes | In addition, we randomly choose another 600 true and 600 false splice sites as validation data in 5 case and 3 case, respectively. and We randomly partition the data into the training and test sets for 10 times, and report the average results as well as standard deviations over the 10 repetitions. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | A modified open-source software named SPAMS from http://spams-devel.gforge.inria.fr/ is used to implement our algorithm. |
| Experiment Setup | Yes | The control parameter of λ in Eq.1 is tuned on the validation data. and The Group and Graph Structures: Based on the priori properties, we generate a graph where the first 10 groups are connected as a path and the cost of each edge on this path is 0.05. Other groups are isolated in the graph. For the edges of the source node s and the sink node t, we set {csu = 0 |u V } and {cut = 1 |u V }. |