Imputation of missing values in multi-view data

• URL: http://arxiv.org/abs/2210.14484v4
• Date: Thu, 20 Jun 2024 12:18:33 GMT
• Title: Imputation of missing values in multi-view data
• Authors: Wouter van Loon, Marjolein Fokkema, Frank de Vos, Marisa Koini, Reinhold Schmidt, Mark de Rooij,
• Abstract summary: We introduce a new imputation method based on the existing stacked penalized logistic regression algorithm for multi-view learning. We compare the performance of the new imputation method with several existing imputation algorithms in simulated data sets and a real data application.
• Score: 0.24739484546803336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data for which a set of objects is described by multiple distinct feature sets (called views) is known as multi-view data. When missing values occur in multi-view data, all features in a view are likely to be missing simultaneously. This may lead to very large quantities of missing data which, especially when combined with high-dimensionality, can make the application of conditional imputation methods computationally infeasible. However, the multi-view structure could be leveraged to reduce the complexity and computational load of imputation. We introduce a new imputation method based on the existing stacked penalized logistic regression (StaPLR) algorithm for multi-view learning. It performs imputation in a dimension-reduced space to address computational challenges inherent to the multi-view context. We compare the performance of the new imputation method with several existing imputation algorithms in simulated data sets and a real data application. The results show that the new imputation method leads to competitive results at a much lower computational cost, and makes the use of advanced imputation algorithms such as missForest and predictive mean matching possible in settings where they would otherwise be computationally infeasible.

Related papers

• An Efficient Algorithm for Clustered Multi-Task Compressive Sensing [60.70532293880842]
  Clustered multi-task compressive sensing is a hierarchical model that solves multiple compressive sensing tasks. The existing inference algorithm for this model is computationally expensive and does not scale well in high dimensions. We propose a new algorithm that substantially accelerates model inference by avoiding the need to explicitly compute these covariance matrices.
  arXiv  Detail & Related papers  (2023-09-30T15:57:14Z)
• Learning to Bound Counterfactual Inference in Structural Causal Models from Observational and Randomised Data [64.96984404868411]
  We derive a likelihood characterisation for the overall data that leads us to extend a previous EM-based algorithm. The new algorithm learns to approximate the (unidentifiability) region of model parameters from such mixed data sources. It delivers interval approximations to counterfactual results, which collapse to points in the identifiable case.
  arXiv  Detail & Related papers  (2022-12-06T12:42:11Z)
• Leachable Component Clustering [10.377914682543903]
  In this work, a novel approach to clustering of incomplete data, termed leachable component clustering, is proposed. The proposed method handles data imputation with Bayes alignment, and collects the lost patterns in theory. Experiments on several artificial incomplete data sets demonstrate that, the proposed method is able to present superior performance compared with other state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2022-08-28T13:13:17Z)
• Principal Component Analysis based frameworks for efficient missing data imputation algorithms [3.635056427544418]
  We propose Principal Component Analysis Imputation (PCAI) to speed up the imputation process and alleviate memory issues of many available imputation techniques. Next, we introduce PCA Imputation - Classification (PIC), an application of PCAI for classification problems with some adjustments. We validate our approach by experiments on various scenarios, which shows that PCAI and PIC can work with various imputation algorithms.
  arXiv  Detail & Related papers  (2022-05-30T14:47:27Z)
• Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets [53.34152466646884]
  In this paper, we show how bringing recent results on equivariant representation learning instantiated on structured spaces together with simple use of classical results on causal inference provides an effective practical solution. We demonstrate how our model allows dealing with more than one nuisance variable under some assumptions and can enable analysis of pooled scientific datasets in scenarios that would otherwise entail removing a large portion of the samples.
  arXiv  Detail & Related papers  (2022-03-29T04:54:06Z)
• Minimax rate of consistency for linear models with missing values [0.0]
  Missing values arise in most real-world data sets due to the aggregation of multiple sources and intrinsically missing information (sensor failure, unanswered questions in surveys...). In this paper, we focus on the extensively-studied linear models, but in presence of missing values, which turns out to be quite a challenging task. This eventually requires to solve a number of learning tasks, exponential in the number of input features, which makes predictions impossible for current real-world datasets.
  arXiv  Detail & Related papers  (2022-02-03T08:45:34Z)
• MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms [82.90843777097606]
  We propose a causally-aware imputation algorithm (MIRACLE) for missing data. MIRACLE iteratively refines the imputation of a baseline by simultaneously modeling the missingness generating mechanism. We conduct extensive experiments on synthetic and a variety of publicly available datasets to show that MIRACLE is able to consistently improve imputation.
  arXiv  Detail & Related papers  (2021-11-04T22:38:18Z)
• Missing Value Imputation on Multidimensional Time Series [16.709162372224355]
  We present DeepMVI, a deep learning method for missing value imputation in multidimensional time-series datasets. DeepMVI combines fine-grained and coarse-grained patterns along a time series, and trends from related series across categorical dimensions. Experiments show that DeepMVI is significantly more accurate, reducing error by more than 50% in more than half the cases.
  arXiv  Detail & Related papers  (2021-03-02T09:55:05Z)
• Sparse PCA via $l_{2,p}$-Norm Regularization for Unsupervised Feature Selection [138.97647716793333]
  We propose a simple and efficient unsupervised feature selection method, by combining reconstruction error with $l_2,p$-norm regularization. We present an efficient optimization algorithm to solve the proposed unsupervised model, and analyse the convergence and computational complexity of the algorithm theoretically.
  arXiv  Detail & Related papers  (2020-12-29T04:08:38Z)
• Berrut Approximated Coded Computing: Straggler Resistance Beyond Polynomial Computing [34.69732430310801]
  We propose Berrut Approximated Coded Computing (BACC) as an alternative approach to deal with stragglers effect. BACC is proven to be numerically stable with low computational complexity. In particular, BACC is used to train a deep neural network on a cluster of servers.
  arXiv  Detail & Related papers  (2020-09-17T14:23:38Z)
• Evolving Losses for Unsupervised Video Representation Learning [91.2683362199263]
  We present a new method to learn video representations from large-scale unlabeled video data. The proposed unsupervised representation learning results in a single RGB network and outperforms previous methods.
  arXiv  Detail & Related papers  (2020-02-26T16:56:07Z)

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.