Scalable Sequential Spectral Clustering
Authors: Yeqing Li, Junzhou Huang, Wei Liu
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments carried out on large datasets demonstrate that the proposed sequential SC algorithm is up to a thousand times faster than the state-of-the-arts. |
| Researcher Affiliation | Collaboration | 1University of Texas at Arlington, Texas, USA 2Didi Research, Bejing, China |
| Pseudocode | Yes | Algorithm 1 Sequential K-Means (Seq KM), Algorithm 2 Sequential Singular Value Decomposition (SSVD), Algorithm 3 Sequential Spectral Clustering (Seq SC) |
| Open Source Code | No | No explicit statement or link for the open-source code of the proposed method was found. |
| Open Datasets | Yes | MNIST1. This dataset consists of 70,000 images of handwritten digits from 0 to 9. ... Cov Type. This dataset consists of 581,012 for predicting the forest cover type from cartographic variables. ... MNIST8m2. This data set consists of 8,100,000 images of handwritten digits from 0 to 9 (Loosli, Canu, and Bottou 2007). |
| Dataset Splits | No | The paper mentions evaluating performance and comparing algorithms but does not explicitly provide training/validation/test dataset splits with percentages or sample counts, nor does it refer to predefined splits with citations for reproducibility. |
| Hardware Specification | Yes | All our experiments are conducted on a desktop computer with a 3.0GHz Intel Pentium 4 CPU and 1GB RAM, Mat Lab 7.14 (32bit). |
| Software Dependencies | Yes | Mat Lab 7.14 (32bit) |
| Experiment Setup | Yes | For parameters of our algorithm, we fix m = 200 and construct 5-NN anchor graph. For all experiments, we fix each block to contain 5000 data points. |