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..
Structure Aware L1 Graph for Data Clustering
Authors: Shuchu Han, Hong Qin
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Results To evaluate the performance of our proposed algorithm, we exam it through spectral clustering applications and compare it to different graphs: Gaussian similarity (GS) graph and L1 graph. Six UCI datasets are selected. The clustering performance is measured by Normalized Mutual Information(NMI) and Accuracy(AC). In our experiment setting, we select α = 0.99 for manifold ranking, and K equals to 10%,20% and 30% percent of total number of data samples. Our experiment results show that SA-L1 graph has better clustering performance than L1 graph generally. |
| Researcher Affiliation | Academia | Shuchu Han, Hong Qin Computer Science Department Stony Brook University Stony Brook, NY 11790 |
| Pseudocode | Yes | Algorithm 1: SA-L1 graph Input :Data samples X = [x1, x2, , xn], where xi X; Parameter K; Output:Adjacency matrix W of sparse graph. 1 Calculate the manifold ranking score matrix F; 2 Normalize the data sample xi with xi 2 = 1; 3 for xi X do 4 Select top K atoms from F(i), and build ˆΦ i ; 5 Solve: min αi αi 1, s.t. xi = ˆΦ iαi, αi 0; 6 W(i, :) = αi; 8 return W; |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Six UCI datasets are selected. |
| Dataset Splits | No | The paper mentions 'Six UCI datasets are selected' but does not provide specific data split information for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models or memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | In our experiment setting, we select α = 0.99 for manifold ranking, and K equals to 10%,20% and 30% percent of total number of data samples. |