GOLFS: Feature Selection via Combining Both Global and Local Information for High Dimensional Clustering

• URL: http://arxiv.org/abs/2507.10956v1
• Date: Tue, 15 Jul 2025 03:39:07 GMT
• Title: GOLFS: Feature Selection via Combining Both Global and Local Information for High Dimensional Clustering
• Authors: Zhaoyu Xing, Yang Wan, Juan Wen, Wei Zhong,
• Abstract summary: We propose a new unsupervised feature selection method, named GlObal and Local information combined Feature Selection (GOLFS)<n>GOLFS combines both local geometric structure via manifold learning and global correlation structure of samples to select the discriminative features.<n>The combination improves the accuracy of both feature selection and clustering by exploiting more comprehensive information.
• Score: 10.740524877905685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is important to identify the discriminative features for high dimensional clustering. However, due to the lack of cluster labels, the regularization methods developed for supervised feature selection can not be directly applied. To learn the pseudo labels and select the discriminative features simultaneously, we propose a new unsupervised feature selection method, named GlObal and Local information combined Feature Selection (GOLFS), for high dimensional clustering problems. The GOLFS algorithm combines both local geometric structure via manifold learning and global correlation structure of samples via regularized self-representation to select the discriminative features. The combination improves the accuracy of both feature selection and clustering by exploiting more comprehensive information. In addition, an iterative algorithm is proposed to solve the optimization problem and the convergency is proved. Simulations and two real data applications demonstrate the excellent finite-sample performance of GOLFS on both feature selection and clustering.

Related papers

• CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection [54.85000884785013]
  Anomaly detection is a complex problem due to the ambiguity in defining anomalies, the diversity of anomaly types, and the scarcity of training data.<n>We propose CLIPfusion, a method that leverages both discriminative and generative foundation models.<n>We believe that our method underscores the effectiveness of multi-modal and multi-model fusion in tackling the multifaceted challenges of anomaly detection.
  arXiv  Detail & Related papers  (2025-06-13T13:30:15Z)
• Bi-Level Unsupervised Feature Selection [11.383408944117804]
  We propose a novel bi-level unsupervised feature selection (BLUFS) method, including a clustering level and a feature level.<n>At the clustering level, spectral clustering is used to generate pseudo-labels for representing the data structure, while a continuous linear regression model is developed to learn the projection matrix.<n>At the feature level, the $ell_2,0$-norm constraint is imposed on the projection matrix for more effectively selecting features.
  arXiv  Detail & Related papers  (2025-05-26T22:52:31Z)
• Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning [53.527506374566485]
  We propose a novel Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning cluster framework, namely AR-DBSCAN.<n>We show that AR-DBSCAN not only improves clustering accuracy by up to 144.1% and 175.3% in the NMI and ARI metrics, respectively, but also is capable of robustly finding dominant parameters.
  arXiv  Detail & Related papers  (2025-05-07T11:37:23Z)
• Cascaded two-stage feature clustering and selection via separability and consistency in fuzzy decision systems [8.048511956662336]
  Feature selection is a vital technique in machine learning, as it can reduce computational complexity, improve model performance, and mitigate the risk of overfitting. This paper proposes a cascaded two-stage feature clustering and selection algorithm for fuzzy decision systems. The effectiveness of our proposed algorithm is evaluated through experiments conducted on 18 public datasets and a real-world schizophrenia dataset.
  arXiv  Detail & Related papers  (2024-07-22T02:44:32Z)
• GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning [84.54244771470012]
  Source-Free Universal Domain Adaptation (SF-UniDA) aims to accurately classify "known" data belonging to common categories. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm. We evolve GLC to GLC++, integrating a contrastive affinity learning strategy.
  arXiv  Detail & Related papers  (2024-03-21T13:57:45Z)
• Feature Selection as Deep Sequential Generative Learning [50.00973409680637]
  We develop a deep variational transformer model over a joint of sequential reconstruction, variational, and performance evaluator losses. Our model can distill feature selection knowledge and learn a continuous embedding space to map feature selection decision sequences into embedding vectors associated with utility scores.
  arXiv  Detail & Related papers  (2024-03-06T16:31:56Z)
• Reinforcement Graph Clustering with Unknown Cluster Number [91.4861135742095]
  We propose a new deep graph clustering method termed Reinforcement Graph Clustering. In our proposed method, cluster number determination and unsupervised representation learning are unified into a uniform framework. In order to conduct feedback actions, the clustering-oriented reward function is proposed to enhance the cohesion of the same clusters and separate the different clusters.
  arXiv  Detail & Related papers  (2023-08-13T18:12:28Z)
• Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection [19.066989850964756]
  We introduce a discriminative clustering model trying to maximise a geometry-aware generalisation of the mutual information called GEMINI. This algorithm avoids the burden of feature exploration and is easily scalable to high-dimensional data and large amounts of samples while only designing a discriminative clustering model. Our results show that Sparse GEMINI is a competitive algorithm and has the ability to select relevant subsets of variables with respect to the clustering without using relevance criteria or prior hypotheses.
  arXiv  Detail & Related papers  (2023-02-07T10:52:04Z)
• Deep Attention-guided Graph Clustering with Dual Self-supervision [49.040136530379094]
  We propose a novel method, namely deep attention-guided graph clustering with dual self-supervision (DAGC) We develop a dual self-supervision solution consisting of a soft self-supervision strategy with a triplet Kullback-Leibler divergence loss and a hard self-supervision strategy with a pseudo supervision loss. Our method consistently outperforms state-of-the-art methods on six benchmark datasets.
  arXiv  Detail & Related papers  (2021-11-10T06:53:03Z)
• Attention-driven Graph Clustering Network [49.040136530379094]
  We propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN) AGCN exploits a heterogeneous-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature. AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion.
  arXiv  Detail & Related papers  (2021-08-12T02:30:38Z)
• Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection [11.214334712819396]
  How to select the uncorrelated and discriminative features is the key problem of unsupervised feature selection. We present a novel generalized regression model imposed by an uncorrelated constraint and the $ell_2,1$-norm regularization. It can simultaneously select the uncorrelated and discriminative features as well as reduce the variance of these data points belonging to the same neighborhood.
  arXiv  Detail & Related papers  (2020-12-27T09:07:26Z)
• Joint Adaptive Graph and Structured Sparsity Regularization for Unsupervised Feature Selection [6.41804410246642]
  We propose a joint adaptive graph and structured sparsity regularization unsupervised feature selection (JASFS) method. A subset of optimal features will be selected in group, and the number of selected features will be determined automatically. Experimental results on eight benchmarks demonstrate the effectiveness and efficiency of the proposed method.
  arXiv  Detail & Related papers  (2020-10-09T08:17:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.