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..
Scalable Sequential Spectral Clustering
Authors: Yeqing Li, Junzhou Huang, Wei Liu
AAAI 2016 | Venue PDF | 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. |