A survey on metric learning for feature vectors and structured data
The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …
Deep relational metric learning
This paper presents a deep relational metric learning (DRML) framework for image
clustering and retrieval. Most existing deep metric learning methods learn an embedding …
clustering and retrieval. Most existing deep metric learning methods learn an embedding …
Deep compositional metric learning
In this paper, we propose a deep compositional metric learning (DCML) framework for
effective and generalizable similarity measurement between images. Conventional deep …
effective and generalizable similarity measurement between images. Conventional deep …
[HTML][HTML] The Development of Hyperspectral Distribution Maps to Predict the Content and Distribution of Nitrogen and Water in Wheat (Triticum aestivum)
Quantifying plant water content and nitrogen levels and determining water and nitrogen
phenotypes is important for crop management and achieving optimal yield and quality …
phenotypes is important for crop management and achieving optimal yield and quality …
Locally adaptive translation for knowledge graph embedding
Abstract Knowledge graph embedding aims to represent entities and relations in a large-
scale knowledge graph as elements in a continuous vector space. Existing methods, eg …
scale knowledge graph as elements in a continuous vector space. Existing methods, eg …
Parametric local metric learning for nearest neighbor classification
J Wang, A Kalousis, A Woznica - Advances in neural …, 2012 - proceedings.neurips.cc
We study the problem of learning local metrics for nearest neighbor classification. Most
previous works on local metric learning learn a number of local unrelated metrics. While …
previous works on local metric learning learn a number of local unrelated metrics. While …
Estimation of nitrogen content in wheat using indices derived from RGB and thermal infrared imaging
R Li, D Wang, B Zhu, T Liu, C Sun, Z Zhang - Field Crops Research, 2022 - Elsevier
The important period of wheat grain accumulation is from the flowering stage to the filling
stage, and the nitrogen content of wheat in this period is of great significance to the yield …
stage, and the nitrogen content of wheat in this period is of great significance to the yield …
[HTML][HTML] Fast generalization rates for distance metric learning: Improved theoretical analysis for smooth strongly convex distance metric learning
Distance metric learning (DML) aims to find a suitable measure to compute a distance
between instances. Facilitated by side information, the learned metric can often improve the …
between instances. Facilitated by side information, the learned metric can often improve the …
Gender and gaze gesture recognition for human-computer interaction
The identification of visual cues in facial images has been widely explored in the broad area
of computer vision. However theoretical analyses are often not transformed into widespread …
of computer vision. However theoretical analyses are often not transformed into widespread …
What makes objects similar: A unified multi-metric learning approach
Linkages are essentially determined by similarity measures that may be derived from
multiple perspectives. For example, spatial linkages are usually generated based on …
multiple perspectives. For example, spatial linkages are usually generated based on …