Optimal subsampling for softmax regression
WebFor softmax regression, the optimal subsampling algorithm has been investigated in [1] under the baseline constraint, where one dimension of the multivariate response variable … WebDec 1, 2024 · Model constraints independent optimal subsampling probabilities for softmax regression December 2024 Authors: Yaqiong Yao Jiahui Zou Haiying Wang University of …
Optimal subsampling for softmax regression
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WebThis idea was generalized in [11] to softmax regression. An optimal subsampling method under the A-optimality criterion (OSMAC) for logistic re-gression inspired by the idea of optimal design of experiments was developed in [12]. They proposed to use a pilot subsample to estimate the optimal subsampling probabilities, which WebDec 18, 2024 · To meet the challenge of massive data, Wang et al. (J Am Stat Assoc 113(522):829–844, 2024b ) developed an optimal subsampling method for logistic …
WebApr 21, 2009 · The model is an extension of the clustered ordinal regression approach of Hedeker and Gibbons that includes the continuous outcome. To handle subsampling, we then derive a partial likelihood (PL) that is based on the bivariate model, and we give an expression for the PL score in Section 3. We show that consistent estimates can be … WebSep 17, 2015 · On the basis of the criterion, we provide two novel subsampling methods, the optimal subsampling and the predictor- length subsampling methods. The predictor …
WebThe purpose of this paper is to extend their method to softmax regression, which is also called multinomial logistic regression and is commonly used to model data with multiple … WebJul 21, 2024 · Two-step algorithm in implementing πLopt i. Step 1. Using the uniform sampling probability π0 i = 1 / N, draw a random subsample of size n0 to obtain a preliminary estimate of β, ˜β0. Replace β with ˜β0 in ( 8) to obtain the approximate optimal subsampling probabilities πLopt, ˜β0i. Step 2.
WebConstruct optimal subsampling probabilities that are invariant to model constraints. ... The optimal subsampling probabilities for softmax regression have been studied under the baseline constraint which treats one dimension of the multivariate response differently from other dimensions. In this paper, we show that different model constraints ...
WebJul 1, 2024 · The information-based optimal subdata selection (IBOSS) is a computationally efficient method to select informative data points from large data sets through processing full data by columns. However, when the volume of a data set is too large to be processed in the available memory of a machine, it is infeasible to implement the IBOSS procedure. bit o honey ice creamWebSubsampling techniques are efficient methods for handling big data. Quite a few optimal sampling methods have been developed for parametric models in which the loss … datagridview header font size c#WebNov 5, 2024 · Title: Optimal Poisson Subsampling for Softmax Regression Authors: Yaqiong Yao, Jiahui Zou Award ID(s): 2105571 Publication Date: 2024-11-05 NSF-PAR ID: … bit o honey jingleWebDec 1, 2024 · Wang H Ma Y Optimal subsampling for quantile regression in big data Biometrika 2024 108 1 99 112 4226192 10.1093/biomet/asaa043 1462.62248 Google Scholar; Wang H Zhu R Ma P Optimal subsampling for large sample logistic regression J. Am. Stat. Assoc. 2024 113 522 829 844 3832230 10.1080/01621459.2024.1292914 … datagridview header color vb.netWebDec 1, 2024 · This paper focuses on a model-free subsampling method, called global likelihood subsampling, such that the subsample is robust to different model choices. It leverages the idea of the global... datagridview header paintWebThis idea was generalized in [11] to softmax regression. An optimal subsampling method under the A-optimality criterion (OSMAC) for logistic re-gression inspired by the idea of … bitokin.comWebSoftmax regression, a generalization of Logistic re-gression (LR) in the setting of multi-class classi-cation, has been widely used in many machine learning applications. However, the performance of softmax regression is extremely sensitive to the presence of noisy data and outliers. To address this issue, we propose a model of robust softmax ... bit o honey jingle lyrics