.png){kind=icon}
返回
Variable star classification using multiview metric learning
.png){kind=icon}
OA
AI
DOI：10.1093/mnras/stz3165.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Comprehensive observations of variable stars can include time domain photometry in a multitude of filters, spectroscopy, estimates of colour (e.g. U-B), etc. When the objective is to classify variable stars, traditional machine learning techniques distill these various representations (or views) into a single feature vector and attempt to discriminate among desired categories. In this work, we propose an alternative approach that inherently leverages multiple views of the same variable star. Our multiview metric learning framework enables robust characterization of star categories by directly learning to discriminate in a multifaceted feature space, thus, eliminating the need to combine feature representations prior to fitting the machine learning model. We also demonstrate how to extend standard multiview learning, which employs multiple vectorized views, to the matrix-variate case which allows very novel variable star signature representations. The performance of our proposed methods is evaluated on the UCR Starlight and LINEAR data sets. Both the vector and matrix-variate versions of our multiview learning framework perform favourably - demonstrating the ability to discriminate variable star categories.
Keyword:
methods: data analysis
methods: statistical
techniques: photometric
期刊
IF:
4.8
论文数:
7.1W
被引数:
25.0W
机构
引用论文
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis基于混合核典型相关分析的降维方法.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Emulsion liquid membrane extraction of lactic acid from aqueous solutions and fermentation broth
PREAI
分享收藏
查看更多内容