Papers › 10,000+ Times Accelerated Robust Subset Selection (ARSS)
10,000+ Times Accelerated Robust Subset Selection (ARSS)
Feiyun Zhu, Bin Fan, Xinliang Zhu, Ying Wang, Shiming Xiang, Chunhong Pan
Subset selection from massive data with noised information is increasingly popular for various applications. This problem is still highly challenging as current methods are generally slow in speed and sensitive to outliers. To address the above two issues, we propose an accelerated robust subset selection (ARSS) method. Specifically in the subset selection area, this is the first attempt to employ the ℓₚ(0<p≤1)-norm based measure for the representation loss, preventing large errors from dominating our objective. As a result, the robustness against outlier elements is greatly enhanced. Actually, data size is generally much larger than feature length, i.e. N≫L. Based on this observation, we propose a speedup solver (via ALM and equivalent derivations) to highly reduce the computational cost, theoretically from O(N⁴) to O(N²L). Extensive experiments on ten benchmark datasets verify that our method not only outperforms state of the art methods, but also runs 10,000+ times faster than the most related method.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multimodal Sentiment Analysis | CMU-MOSI | MCEN | Acc-2 | 87.35 | #2 of 12 | Archive leaderboard | report |
| Multimodal Sentiment Analysis | CMU-MOSI | MCEN | Acc-5 | 58.02 | #2 of 12 | Archive leaderboard | report |
| Multimodal Sentiment Analysis | CMU-MOSI | MCEN | Acc-7 | 50.58 | #2 of 12 | Archive leaderboard | report |
| Multimodal Sentiment Analysis | CMU-MOSI | MCEN | Corr | 0.813 | #2 of 12 | Archive leaderboard | report |
| Multimodal Sentiment Analysis | CMU-MOSI | MCEN | F1 | 87.48 | #2 of 12 | Archive leaderboard | report |
| Multimodal Sentiment Analysis | CMU-MOSI | MCEN | MAE | 0.678 | #2 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections