Prediction and estimation consistency of sparse multi-class penalized optimal scoring

被引：5

作者：

Gaynanova, Irina ^{[1
]}

机构：

[1] Texas A&M Univ, Dept Stat, MS 3143, College Stn, TX 77843 USA

来源：

BERNOULLI | 2020年 / 26卷 / 01期

基金：

美国国家科学基金会;

关键词：

classification; high-dimensional regression; lasso; linear discriminant analysis; ORACLE INEQUALITIES; DISCRIMINANT-ANALYSIS; VARIABLE SELECTION; MODEL SELECTION; REGRESSION; RECOVERY;

D O I：

10.3150/19-BEJ1126

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Sparse linear discriminant analysis via penalized optimal scoring is a successful tool for classification in high-dimensional settings. While the variable selection consistency of sparse optimal scoring has been established, the corresponding prediction and estimation consistency results have been lacking. We bridge this gap by providing probabilistic bounds on out-of-sample prediction error and estimation error of multi-class penalized optimal scoring allowing for diverging number of classes.

引用

页码：286 / 322

页数：37

共 50 条

[41] Comparing and Analysis of Different Optimization Techniques on Sparse Multi-Class Data
Panda, Digbijay
Singh, Sanika
Mukherjee, Saurabh
Chakraborty, Sudeshna
PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND KNOWLEDGE ECONOMY (ICCIKE' 2019), 2019, : 528 - 531
[42] Learning of a multi-class classifier with rejection option using Sparse Representation
Kang, Jungyu
Yoo, Chang D.
18TH IEEE INTERNATIONAL SYMPOSIUM ON CONSUMER ELECTRONICS (ISCE 2014), 2014,
[43] A Sparse Multi-class Least-Squares Support Vector Machine
Xia, Xiao-Lei
Li, Kang
2008 IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS, VOLS 1-5, 2008, : 648 - 653
[44] Multi-class feature selection via Sparse Softmax with a discriminative regularization
Sun, Zhenzhen
Chen, Zexiang
Liu, Jinghua
Yu, Yuanlong
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2025, 16 (01) : 159 - 172
[45] SPARSE LEAST SQUARES K-SVCR MULTI-CLASS CLASSIFICATION
Moosaei, Hossein
JOURNAL OF NONLINEAR AND VARIATIONAL ANALYSIS, 2024, 8 (06): : 953 - 971
[46] Sparse feature learning for multi-class Parkinson's disease classification
Lei, Haijun
Zhao, Yujia
Wen, Yuting
Luo, Qiuming
Cai, Ye
Liu, Gang
Lei, Baiying
TECHNOLOGY AND HEALTH CARE, 2018, 26 : S193 - S203
[47] GAP Safe screening rules for sparse multi-task and multi-class models
Ndiaye, Eugene
Fercoq, Olivier
Gramfort, Alexandre
Salmon, Joseph
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 28 (NIPS 2015), 2015, 28
[48] OFES: Optimal feature evaluation and selection for multi-class classification
Ram, Vallam Sudhakar Sai
Kayastha, Namrata
Sha, Kewei
DATA & KNOWLEDGE ENGINEERING, 2022, 139
[49] Learning Imbalanced Multi-class Data with Optimal Dichotomy Weights
Liu, Xu-Ying
Li, Qian-Qian
Zhou, Zhi-Hua
2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), 2013, : 478 - 487
[50] Optimal Policy for Multi-Class Scheduling in a Single Server Queue
Osipova, Natalia
Ayesta, Urtzi
Avrachenkov, Konstantin
2009 21ST INTERNATIONAL TELETRAFFIC CONGRESS (ITC 21), 2009, : 295 - +

← 1 2 3 4 5 →