Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection
A recent paper highlights the necessity of establishing a baseline for evaluating unsupervised feature selection methods. The authors propose using random feature selection as a benchmark, revealing that many advanced methods do not outperform this baseline. This emphasizes the need for consistent improvement over random selection in future developments.
- ▪The paper suggests that many unsupervised feature selection methods are evaluated without a proper baseline.
- ▪Using random feature selection as a baseline can help assess the effectiveness of new methods.
- ▪The authors found that some state-of-the-art methods were outperformed by random selection in terms of performance and efficiency.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.22973 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | ciTRjMyxEInc |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Machine Learning arXiv:2605.22973 (cs) [Submitted on 21 May 2026] Title:Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection Authors:Muhammad Rajabinasab, Michael E. Houle, Oussama Chelly, Arthur Zimek View a PDF of the paper titled Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection, by Muhammad Rajabinasab and 3 other authors View PDF HTML (experimental) Abstract:Many novel unsupervised feature selection methods are proposed each year, yet their empirical evaluation is limited to supervised and unsupervised evaluation metrics computed on selected datasets, along with comparisons to existing methods.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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