Improved cross-validated distances for multivariate pattern analysis

• URL: http://arxiv.org/abs/2608.10394v1
• Date: Tue, 11 Aug 2026 02:41:43 GMT
• Title: Improved cross-validated distances for multivariate pattern analysis
• Abstract summary: Guggenmos et al. recommended the use of the cross-validated Euclidean distance or the within-class-corrected Pearson distance.<n>We show that we can improve upon these distances.
• Score: 0.42970700836450487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Characterizing the dissimilarity of neural representations between experimental conditions, and tracking it across time, is a central goal of multivariate pattern analysis. Guggenmos et al. (2018) assessed the reliability of many of the measures that can be used for that purpose on MEG data and recommended the use of either the cross-validated Euclidean distance or the within-class-corrected Pearson distance. In this commentary, we show that we can improve upon these distances. First, we show that the cross-validated Euclidean distance is equivalent to a sum of between-partition distances and that this equivalence can be leveraged to obtain a generalized variant, with increased reliability and accuracy. Second, we use the relationship between Euclidean distance and Pearson correlation to define a cross-validated correlation distance in a similar way. The resulting distance is more accurate and interpretable than a formulation proposed by Guggenmos and colleagues. Finally, we discuss the relationship between our generalized cross-validation and within-class correction, another strategy often used to increase reliability, and we show that generalized cross-validation results in higher accuracy for the correlation distance.

Related papers

• Comparing Labeled Markov Chains: A Cantor-Kantorovich Approach [53.66196601631798]
  We study the recently introduced Cantor-Kantorovich (or CK) distance.<n>In particular we show that the latter can be framed as a discounted sum of finite-horizon Total Variation distances.<n>More precisely, we show that the exact computation of the CK distance is #P-hard.
  arXiv  Detail & Related papers  (2025-11-22T16:02:56Z)
• GeoMM: On Geodesic Perspective for Multi-modal Learning [55.41612200877861]
  This paper introduces geodesic distance as a novel distance metric in multi-modal learning for the first time.<n>Our approach incorporates a comprehensive series of strategies to adapt geodesic distance for the current multimodal learning.
  arXiv  Detail & Related papers  (2025-05-16T13:12:41Z)
• Efficient and Stable Multi-Dimensional Kolmogorov-Smirnov Distance [7.326930455001404]
  We revisit extending the Kolmogorov-Smirnov distance between probability distributions to the multidimensional setting.<n>We prove that the distance between a distribution and a sample from the distribution converges to 0 as the sample size grows.
  arXiv  Detail & Related papers  (2025-04-15T15:42:49Z)
• Consistent Estimation of a Class of Distances Between Covariance Matrices [7.291687946822539]
  We are interested in the family of distances that can be expressed as sums of traces of functions that are separately applied to each covariance matrix. A statistical analysis of the behavior of this class of distance estimators has also been conducted. We present a central limit theorem that establishes the Gaussianity of these estimators and provides closed form expressions for the corresponding means and variances.
  arXiv  Detail & Related papers  (2024-09-18T07:36:25Z)
• Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
  We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data. Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
  arXiv  Detail & Related papers  (2024-04-24T09:04:36Z)
• A Framework for Analyzing Cross-correlators using Price's Theorem and Piecewise-Linear Decomposition [5.094549132183797]
  We present a general mathematical framework that allows us to analyze cross-correlators constructed using a mixture of piece-wise linear functions. We show that some of the most promising cross-correlators are based on Huber's loss functions, margin-propagation (MP) functions, and the log-sum-exp (LSE) functions.
  arXiv  Detail & Related papers  (2023-04-18T19:03:27Z)
• Variable Importance Matching for Causal Inference [73.25504313552516]
  We describe a general framework called Model-to-Match that achieves these goals. Model-to-Match uses variable importance measurements to construct a distance metric. We operationalize the Model-to-Match framework with LASSO.
  arXiv  Detail & Related papers  (2023-02-23T00:43:03Z)
• Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariances [0.6091702876917281]
  We generalize fast leave-one-out formulae to multiple-fold cross-validation. We highlight the covariance structure of cross-validation residuals in both Simple and Universal Kriging frameworks. Our results enable fast multiple-fold cross-validation and have direct consequences in model diagnostics.
  arXiv  Detail & Related papers  (2021-01-08T17:02:37Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)
• Minimax Optimal Estimation of KL Divergence for Continuous Distributions [56.29748742084386]
  Esting Kullback-Leibler divergence from identical and independently distributed samples is an important problem in various domains. One simple and effective estimator is based on the k nearest neighbor between these samples.
  arXiv  Detail & Related papers  (2020-02-26T16:37:37Z)
• High-Dimensional Independence Testing via Maximum and Average Distance Correlations [4.872570541276082]
  We characterize the consistency properties of maximum and average distance correlations in high-dimensional settings.<n>We compare the advantages of each test statistic, examine their respective null distributions, and present a fast chi-square-based testing procedure.
  arXiv  Detail & Related papers  (2020-01-04T16:21:50Z)
• MALTS: Matching After Learning to Stretch [86.84454964051014]
  We learn an interpretable distance metric for matching, which leads to substantially higher quality matches. Our ability to learn flexible distance metrics leads to matches that are interpretable and useful for the estimation of conditional average treatment effects.
  arXiv  Detail & Related papers  (2018-11-18T22:29:59Z)

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.