Deep Linear Coding for Fast Graph Clustering
Authors: Ming Shao, Sheng Li, Zhengming Ding, Yun Fu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on clustering tasks demonstrate that our method performs well in terms of both time complexity and clustering accuracy. On a large-scale benchmark dataset (580K), our method runs 1500 times faster than the original spectral clustering. |
| Researcher Affiliation | Academia | Ming Shao, Sheng Li, Zhengming Ding Department of ECE Northeastern University Boston, MA 02115, USA {mingshao,shengli,allanding}@ece.neu.edu Yun Fu Department of ECE, College of CIS Northeastern University Boston, MA 02115, USA {yunfu}@ece.neu.edu |
| Pseudocode | Yes | Algorithm 1: Algorithm of Single-layer Linear Coding. Algorithm 2: Algorithm of Deep Linear Coding (DLC). |
| Open Source Code | No | The paper does not provide any concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | Corel The dataset has been widely used in computer vision and image processing. Coil20 An object image database with 20 different objects. Yale B This database is popular in face recognition algorithms evaluations... Pendigit This is a handwritten digit data set... Letter The dataset consists of 26 capital letters... Mnist Another handwritten digits benchmark dataset widely used in clustering evaluations. Covtype A large scale scientific dataset... |
| Dataset Splits | No | The paper uses various datasets for evaluation but does not specify explicit train/validation/test dataset splits or cross-validation setup for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Matlab implementation' and 'FLANN library' but does not provide specific version numbers for these or any other software dependencies, making the environment unreproducible. |
| Experiment Setup | Yes | We set the number of neighbors in k NN search at 5, and the number of landmarks in the first and second layers at 1000 unless otherwise specified. In addition, we set both the balancing parameter λ and Gaussian kernel bandwidth σ at 1. To balance the performance and speed, the number of iterations in each layer is set to T = 5. |