Distributed Sparse Multicategory Discriminant Analysis

• URL: http://arxiv.org/abs/2202.10913v1
• Date: Tue, 22 Feb 2022 14:23:33 GMT
• Title: Distributed Sparse Multicategory Discriminant Analysis
• Authors: Hengchao Chen, Qiang Sun
• Abstract summary: This paper proposes a convex formulation for sparse multicategory linear discriminant analysis and then extend it to the distributed setting when data are stored across multiple sites. Theoretically, we establish statistical properties ensuring that the distributed sparse multicategory linear discriminant analysis performs as good as the centralized version after a few rounds of communications.
• Score: 1.7223564681760166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a convex formulation for sparse multicategory linear discriminant analysis and then extend it to the distributed setting when data are stored across multiple sites. The key observation is that for the purpose of classification it suffices to recover the discriminant subspace which is invariant to orthogonal transformations. Theoretically, we establish statistical properties ensuring that the distributed sparse multicategory linear discriminant analysis performs as good as the centralized version after {a few rounds} of communications. Numerical studies lend strong support to our methodology and theory.

Related papers

• Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification [72.77513633290056]
  We present a novel approach that combines the eigenanalysis of a covariance matrix evaluated on a training set with a Hessian matrix evaluated on a deep learning model. Our method captures intricate patterns and relationships, enhancing classification performance.
  arXiv  Detail & Related papers  (2024-02-14T16:10:42Z)
• Conformal inference for regression on Riemannian Manifolds [49.7719149179179]
  We investigate prediction sets for regression scenarios when the response variable, denoted by $Y$, resides in a manifold, and the covariable, denoted by X, lies in Euclidean space. We prove the almost sure convergence of the empirical version of these regions on the manifold to their population counterparts.
  arXiv  Detail & Related papers  (2023-10-12T10:56:25Z)
• Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model [2.517838307493912]
  This paper proposes an improved linear discriminant analysis called spectrally-corrected and regularized LDA (SRLDA) It is proved that SRLDA has a linear classification global optimal solution under the spiked model assumption. Experiments on different data sets show that the SRLDA algorithm performs better in classification and dimensionality reduction than currently used tools.
  arXiv  Detail & Related papers  (2022-10-08T00:47:50Z)
• Robust classification with flexible discriminant analysis in heterogeneous data [0.7646713951724009]
  This paper presents a new robust discriminant analysis where each data point is drawn by its own arbitrary scale parameter. It is shown that maximum-likelihood parameter estimation and classification are very simple, fast and robust compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-09T09:22:56Z)
• Riemannian-based Discriminant Analysis for Feature Extraction and Classification [2.1485350418225244]
  Discriminant analysis is a widely used approach in machine learning to extract low-dimensional features from the high-dimensional data. Traditional Euclidean-based algorithms for discriminant analysis are easily convergent to a spurious local minima. We propose a novel method named Riemannian-based Discriminant Analysis (RDA), which transforms the traditional Euclidean-based methods to the Riemannian manifold space.
  arXiv  Detail & Related papers  (2021-01-20T09:13:34Z)
• A Compressive Classification Framework for High-Dimensional Data [12.284934135116515]
  We propose a compressive classification framework for settings where the data dimensionality is significantly higher than the sample size. The proposed method, referred to as regularized discriminant analysis (CRDA), is based on linear discriminant analysis. It has the ability to select significant features by using joint-sparsity promoting hard thresholding in the discriminant rule.
  arXiv  Detail & Related papers  (2020-05-09T06:55:00Z)
• Compressing Large Sample Data for Discriminant Analysis [78.12073412066698]
  We consider the computational issues due to large sample size within the discriminant analysis framework. We propose a new compression approach for reducing the number of training samples for linear and quadratic discriminant analysis.
  arXiv  Detail & Related papers  (2020-05-08T05:09:08Z)
• Asymptotic Analysis of an Ensemble of Randomly Projected Linear Discriminants [94.46276668068327]
  In [1], an ensemble of randomly projected linear discriminants is used to classify datasets. We develop a consistent estimator of the misclassification probability as an alternative to the computationally-costly cross-validation estimator. We also demonstrate the use of our estimator for tuning the projection dimension on both real and synthetic data.
  arXiv  Detail & Related papers  (2020-04-17T12:47:04Z)
• Saliency-based Weighted Multi-label Linear Discriminant Analysis [101.12909759844946]
  We propose a new variant of Linear Discriminant Analysis (LDA) to solve multi-label classification tasks. The proposed method is based on a probabilistic model for defining the weights of individual samples. The Saliency-based weighted Multi-label LDA approach is shown to lead to performance improvements in various multi-label classification problems.
  arXiv  Detail & Related papers  (2020-04-08T19:40:53Z)
• Few-shot Domain Adaptation by Causal Mechanism Transfer [107.08605582020866]
  We study few-shot supervised domain adaptation (DA) for regression problems, where only a few labeled target domain data and many labeled source domain data are available. Many of the current DA methods base their transfer assumptions on either parametrized distribution shift or apparent distribution similarities. We propose mechanism transfer, a meta-distributional scenario in which a data generating mechanism is invariant among domains.
  arXiv  Detail & Related papers  (2020-02-10T02:16:53Z)

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.