Unsupervised Feature Selection

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11 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Unsupervised Feature Selection Using Local Intrinsic Dimensionality -- Extended Version
Oct 29, 2026Oussama Chelly, Michael E. Houle, Ken-ichi Kawarabayashi +2Unsupervised Feature Selection Feature Selection
Oratio Technologies · New Jersey Institute of Technology, United States · National Institute of Informatics, Japan +2
Learning to Select Features with Manifold Deep Unfolding PCA
Oct 14, 2026Jingjing Liu, Pei Tai, Xianchao Xiu +1Unsupervised Feature Selection Feature Selection
Shanghai Key Laboratory of Automobile Intelligent Network Interaction Chip and System, School of Microelectronics, Shanghai University, Shanghai 200444, China · School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China · School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China
When Features Become Instances: Inverted Contrastive Learning for Unsupervised Feature Selection
Sep 1, 2026Utsab Ghosh, Roshni ChakrabortyContrastive Learning Unsupervised Feature Selection
ABV-Indian Institute of Information Technology and Management, Gwalior, India.
Towards Truly Unsupervised Evaluation of Feature Selection -- Extended Version
Aug 12, 2026Hafiz Saud Arshad, Muhammad Rajabinasab, Arthur ZimekFeature Selection Unsupervised Feature Selection
University of Southern Denmark, Odense, Denmark
Variance-Preserving Orthogonal Selection (VPOS): Greedy Feature Selection via Orthogonal Deflation in PCA Loading Space
Jul 25, 2026Baran Koseoglu, Berrin YanikogluPrincipal Component Analysis Feature Selection
Independent Researcher, London, UK · Sabanci University, Istanbul, Turkey
Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection -- Extended Version
May 21, 2026Muhammad Rajabinasab, Michael E. Houle, Oussama Chelly +1Unsupervised Learning Unsupervised Feature Selection
University of Southern Denmark, Odense, Denmark · New Jersey Institute of Technology, Newark, NJ, United States · Oratio Technologies, Tunis, Tunisia
Objective-Induced Bias and Search Dynamics in Multiobjective Unsupervised Feature Selection
May 20, 2026Mathieu Cherpitel, Thomas Bäck, Martijn R. Tannemaat +1Unsupervised Feature Selection Feature Selection
1LIACS, Leiden University, Leiden, The Netherlands · 2LUMC, Leiden University, Leiden, The Netherlands
Diffusion-Guided Feature Selection via Nishimori Temperature: Noise-Based Spectral Embedding
Apr 27, 2026Vasiliy S. Usatyuk, Denis A. Sapozhnikov, Sergey I. EgorovFeature Selection Dimensionality Reduction
R&D Department T8 LLC Moscow, Russia · Department of Computer Science South-West State University Kursk, Russia
Unsupervised feature selection using Bayesian Tucker decomposition
Apr 16, 2026Y-h. Taguchi, Yoh-ichi MototakeTensor Decomposition Unsupervised Feature Selection
Department of Physics, Chuo University, Tokyo, 112-8551, Japan. · Graduate School of Social Data Science, Hitotsubashi University, Tokyo, 186-8601, Japan.
i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
Mar 25, 2026Chen Ma, Wanjie Wang, Shuhao FanUnsupervised Clustering Unsupervised Feature Selection
SUSTech · NUS
Scalable unsupervised feature selection via weight stability
Jun 6, 2025Xudong Zhang, Renato Cordeiro de AmorimUnsupervised Learning Clustering
School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe, UK.

Oct 29, 2026J/K move · Enter open · S save

Unsupervised Feature Selection Using Local Intrinsic Dimensionality -- Extended Version

Oussama Chelly, Michael E. Houle, Ken-ichi Kawarabayashi, Muhammad Rajabinasab+1

Oratio Technologies · New Jersey Institute of Technology, United States · National Institute of Informatics, Japan+2

Unsupervised Feature Selection Feature Selection

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High dimensionality degrades both the efficiency and the effectiveness of learning algorithms, and in many applications no class labels are available to guide dimensionality reduction. This paper proposes LIDFS, an unsupervised feature selection method based on Local Intrinsic Dimensionality (LID). The method builds on the formal equivalence between LID and the indiscriminability of distance measures. The data matrix is transposed so that each feature is treated as a point, and the LID of each feature is estimated by maximum likelihood from the distances to its

k

nearest neighboring features. Features with lower LID exhibit greater local discriminability and receive higher importance scores. A refinement step then retains one representative per feature neighborhood and demotes locally redundant features. The result is a complete ranking from which any number of features can be selected without rerunning the procedure. LIDFS does not use any information about labels or the number of classes, and runs in

\mathcal{O}(d^2 n)

time for

n

instances and

d

features. LIDFS is compared with classic and state-of-the-art methods and a random baseline on 23 high-dimensional datasets, the classic benchmark collection. The evaluation uses classification accuracy and AUC with five downstream classifiers, as well as clustering accuracy and normalized mutual information, over both full and aggressive selection ranges. LIDFS ranks first in the supervised evaluations and first or tied second in the clustering-based evaluations. It belongs to the top statistical group together with methods that require the number of classes, and it runs faster than its closest competitor. Several recent methods, by contrast, fail to outperform the random baseline. A sensitivity analysis shows that small neighborhood sizes yield the best results.