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..
Robust Subspace Segmentation by Simultaneously Learning Data Representations and Their Affinity Matrix
Authors: Xiaojie Guo
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on both synthetic and real data demonstrate the efficacy of the proposed method and its superior performance over the state-of-the-art alternatives. |
| Researcher Affiliation | Academia | Xiaojie Guo State Key Laboratory of Information Security Institute of Information Engineering, Chinese Academy of Sciences EMAIL |
| Pseudocode | Yes | Algorithm 1: Proposed Robust Subspace Segmentation |
| Open Source Code | No | The paper mentions that codes for *compared methods* were downloaded from authors' webpages, but there is no statement about the availability of the proposed method's source code. |
| Open Datasets | Yes | We compare the proposed method with other state-of-the-art methods for face clustering on the Extended Yale B dataset [Lee et al., 2005]. Further, we compare the performance of SSC, LRR, LSR, CASS and our method on the USPS dataset5, which consists of 10 classes corresponding to 10 handwritten digits, 0 9. 5www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/multiclass.html. Moreover, we attempt to test the abilities of different approaches on a more challenging dataset UMIST [Graham and Allinson, 1998]. |
| Dataset Splits | No | The paper mentions using "average segmentation accuracies over 10 independent trials" but does not provide specific training, validation, or test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper mentions "Our algorithm takes 4s to finish the computation on our PC", which is a general statement and does not provide specific hardware details (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper mentions using "Normalized Cuts" and other methods' codes downloaded from authors' webpages, but it does not specify any software dependencies with version numbers for its own implementation. |
| Experiment Setup | Yes | To simplify our parameters, we let λ1 = λ2 = λ3 = ˆλ {0.1, 0.2, ..., 1.0}, although the simplification may very likely exclude the best performance for our method. Based on this testing, we will fix k = 3 for our method for the rest experiments. |