.png){kind=icon}
返回
Incremental learning from chunk data for IDR/QR
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2015.01.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
IDR/QR, which is an incremental dimension reduction algorithm based on linear discriminant analysis (LDA) and QR decomposition, has been successfully employed for feature extraction and incremental learning. IDR/QR can update the discriminant vectors with light computation when new training samples are inserted into the training data set. However, IDR/QR has two limitations: 1) IDR/QR can only process new samples one instance after another even if a chunk of training samples is available at a time; and 2) the approximate trick is used in IDR/QR. Then there exists a gap in performance between incremental and batch IDR/QR solutions. To address the problems of IDR/QR, in this paper, we propose a new chunk IDR method which is capable of processing multiple data instances at a time and can accurately update the discriminant vectors when new data items are added dynamically. Experiments on some real databases demonstrate the effectiveness of the proposed algorithm over the original one. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Feature extraction
Dimensionality reduction
Linear discriminant analysis
Small sample size problem
Incremental learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.2
论文数:
4.1K
被引数:
6.7K
机构
引用论文
分享收藏A new and fast implementation for null space based linear discriminant analysis.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Schedulability analysis of non-preemptive strictly periodic tasks in multi-core real-time systems
PREAI
分享收藏
查看更多内容