Feature Selection for Latent Factor Models
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The paper argues that many newly proposed unsupervised feature selection methods are evaluated only against other methods without a proper baseline. Using random feature selection as a baseline, the authors demonstrate that several state‑of‑the‑art methods are actually outperformed in both performance and efficiency. They call for the inclusion of random selection as a baseline and for the development of better evaluation metrics to ensure realistic assessment of future methods.
arXiv:2511. 20851v3 Announce Type: replace-cross Abstract: Feature selection remains difficult in modern high-dimensional settings, and established methods such as Boruta and Recursive Feature Elimination are either computationally costly or lack a statistically justified stopping criterion for their importance scores.
The paper introduces LIDFS, an unsupervised feature selection technique that uses Local Intrinsic Dimensionality (LID) to rank features based on their local discriminability. By treating each feature as a point and estimating its LID via maximum likelihood from distances to its k nearest neighboring features, LIDFS assigns higher importance to features with lower LID and refines the ranking to remove locally redundant features. Experiments on 23 high‑dimensional datasets show that LIDFS outperforms both classic and state‑of‑the‑art methods in supervised and clustering evaluations while running faster than its closest competitor.
arXiv:2609.07493v1 Announce Type: new Abstract: In this paper, we propose a class-wise dimension (channel) selection framework for Multivariate Time Series Classification (MTSC). Rather than applying...
arXiv:2608.12057v4 Announce Type: replace Abstract: Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of eval...
arXiv:2608. 12057v1 Announce Type: new Abstract: Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of evaluation techniques to measure the quality of a specific method.