Unified View Imputation and Feature Selection Learning for Incomplete Multi-view Data

• URL: http://arxiv.org/abs/2401.10549v1
• Date: Fri, 19 Jan 2024 08:26:44 GMT
• Title: Unified View Imputation and Feature Selection Learning for Incomplete Multi-view Data
• Authors: Yanyong Huang, Zongxin Shen, Tianrui Li, Fengmao Lv
• Abstract summary: Multi-view unsupervised feature selection (MUFS) is an effective technology for reducing dimensionality in machine learning. Existing methods cannot directly deal with incomplete multi-view data where some samples are missing in certain views. UNIFIER explores the local structure of multi-view data by adaptively learning similarity-induced graphs from both the sample and feature spaces.
• Score: 13.079847265195127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although multi-view unsupervised feature selection (MUFS) is an effective technology for reducing dimensionality in machine learning, existing methods cannot directly deal with incomplete multi-view data where some samples are missing in certain views. These methods should first apply predetermined values to impute missing data, then perform feature selection on the complete dataset. Separating imputation and feature selection processes fails to capitalize on the potential synergy where local structural information gleaned from feature selection could guide the imputation, thereby improving the feature selection performance in turn. Additionally, previous methods only focus on leveraging samples' local structure information, while ignoring the intrinsic locality of the feature space. To tackle these problems, a novel MUFS method, called UNified view Imputation and Feature selectIon lEaRning (UNIFIER), is proposed. UNIFIER explores the local structure of multi-view data by adaptively learning similarity-induced graphs from both the sample and feature spaces. Then, UNIFIER dynamically recovers the missing views, guided by the sample and feature similarity graphs during the feature selection procedure. Furthermore, the half-quadratic minimization technique is used to automatically weight different instances, alleviating the impact of outliers and unreliable restored data. Comprehensive experimental results demonstrate that UNIFIER outperforms other state-of-the-art methods.

Related papers

• Adapt-$\infty$: Scalable Lifelong Multimodal Instruction Tuning via Dynamic Data Selection [89.42023974249122]
  Adapt-$infty$ is a new multi-way and adaptive data selection approach for Lifelong Instruction Tuning. We construct pseudo-skill clusters by grouping gradient-based sample vectors. We select the best-performing data selector for each skill cluster from a pool of selector experts.
  arXiv  Detail & Related papers  (2024-10-14T15:48:09Z)
• A Contrast Based Feature Selection Algorithm for High-dimensional Data set in Machine Learning [9.596923373834093]
  We propose a novel filter feature selection method, ContrastFS, which selects discriminative features based on the discrepancies features shown between different classes. We validate effectiveness and efficiency of our approach on several widely studied benchmark datasets, results show that the new method performs favorably with negligible computation.
  arXiv  Detail & Related papers  (2024-01-15T05:32:35Z)
• Towards Free Data Selection with General-Purpose Models [71.92151210413374]
  A desirable data selection algorithm can efficiently choose the most informative samples to maximize the utility of limited annotation budgets. Current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selection repeatedly. FreeSel bypasses the heavy batch selection process, achieving a significant improvement in efficiency and being 530x faster than existing active learning methods.
  arXiv  Detail & Related papers  (2023-09-29T15:50:14Z)
• MvFS: Multi-view Feature Selection for Recommender System [7.0190343591422115]
  We propose Multi-view Feature Selection (MvFS), which selects informative features for each instance more effectively. MvFS employs a multi-view network consisting of multiple sub-networks, each of which learns to measure the feature importance of a part of data. MvFS adopts an effective importance score modeling strategy which is applied independently to each field.
  arXiv  Detail & Related papers  (2023-09-05T09:06:34Z)
• infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information [68.76707843019886]
  infoVerse is a universal framework for dataset characterization. infoVerse captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. In three real-world applications (data pruning, active learning, and data annotation), the samples chosen on infoVerse space consistently outperform strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T18:12:48Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Incremental Unsupervised Feature Selection for Dynamic Incomplete Multi-view Data [16.48538951608735]
  In real applications, the multi-view data are often incomplete, i.e., some views of instances are missing. We propose an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I$2$MUFS) on incomplete multi-view streaming data.
  arXiv  Detail & Related papers  (2022-04-05T16:29:39Z)
• Low-rank Dictionary Learning for Unsupervised Feature Selection [11.634317251468968]
  We introduce a novel unsupervised feature selection approach by applying dictionary learning ideas in a low-rank representation. A unified objective function for unsupervised feature selection is proposed in a sparse way by an $ell_2,1$-norm regularization. Our experimental findings reveal that the proposed method outperforms the state-of-the-art algorithm.
  arXiv  Detail & Related papers  (2021-06-21T13:39:10Z)
• Auto-weighted Multi-view Feature Selection with Graph Optimization [90.26124046530319]
  We propose a novel unsupervised multi-view feature selection model based on graph learning. The contributions are threefold: (1) during the feature selection procedure, the consensus similarity graph shared by different views is learned. Experiments on various datasets demonstrate the superiority of the proposed method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-11T03:25:25Z)
• Exploiting Shared Representations for Personalized Federated Learning [54.65133770989836]
  We propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions.
  arXiv  Detail & Related papers  (2021-02-14T05:36:25Z)
• 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.