.png){kind=icon}
返回
Similarity preservation in dimensionality reduction using a kernel-based cost function
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2019.05.009.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Dimensionality reduction aims to preserve, as much as possible, the significant structure of high-dimensional data in the low-dimensional space. This allows removing noise and redundant features, which is useful in exploratory data analysis, classification, and regression tasks. There are two main challenges in dimensionality reduction: (1) how to measure the manifold structures; (2) how to quantify the embedding preservation. On the one hand, previous approaches try to measure the manifold structure using variance, dot product, distance, and similarity preservation. On the other hand, usually, the embedding quality is quantified using divergence-based measures such as Kullback-Leibler and Jensen-Shannon. We propose a dimensionality reduction method that minimizes the mismatch between high and low dimensional spaces. Unlike traditional dimensionality reduction formulations, the proposed approach uses a kernel-based cost function to quantify the embedding quality. Our approach is validated on both synthetic and real-world datasets. In terms of visual inspection and quantitative evaluation of neighborhood preservation, results show that our proposal preserves global data structures in the low-dimensional representation. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Sequential learning
Adaptive learning-rate
Kernel adaptive filters
Correntropy
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:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
Five Proteins of Laodelphax striatellus Are Potentially Involved in the Interactions between Rice Stripe Virus and Vector.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏Kernel-based dimensionality reduction using Renyi's α-entropy measures of similarityNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysisNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Using data dimensionality reduction for recognition of incomplete dynamic gestures基于数据降维的不完整动态手势识别
PREAI
分享收藏
查看更多内容