t-SNE:A study on reducing the dimensionality of hyperspectral data for the regression problem of estimating oenological parameters  

作　　者：Rui Silva Pedro Melo-Pinto 

机构地区：[1]CITAB-Centre for the Research and Technology of Agro-Environmental and Biological Sciences,Inov4Agro-Institute for Innovation,Capacity Building and Sustainability of Agri-Food Production,Universidade de Trás-os-Montes e Alto Douro,Quinta dos Prados,Vila Real 5000-801,Portugal [2]Departamento de Engenharias,Escola de Ciências e Tecnologia,Universidade de Trás-os-Montes e Alto Douro,Quinta dos Prados,Vila Real 5000-801,Portugal

出　　处：《Artificial Intelligence in Agriculture》2023年第1期58-68,共11页农业人工智能（英文）

基　　金：supported by National Funds by FCT-Portuguese Foundation for Science and Technology,under the project UIDB/04033/2020;The authors also gratefully acknowledge the support from National funding by FCT,Portuguese Foundation for Science and Technology,through the individual research grant(SFRH/BD/137216/2018);from NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.

摘　　要：In recent years there is a growing importance in using machine learning techniques to improve procedures in precision agriculture:in this work we perform a study on models capable of predicting oenological parameters from hyperspectral images of wine grape berries,a specially relevant topic to boost production tasks for winemakers.Specifically,we explore the capabilities of a novel technique mostly used for visualization,t-Distributed Stochastic Neighbor Embedding(t-SNE),for reducing the dimensionality of the highly complex hyperspectral data and compare its performance with Principal Component Analysis(PCA)method,which despite the introduction of many nonlinear dimensionality reduction techniques over the years,had achieved the best results for real-world data across several studies in literature.Additionally we explore the potential of Kernel t-SNE,an extension to the t-SNE method that allows for the usage of the technique in streaming data or online scenarios.Our results show that,in a direct comparison,t-SNE achieves better metrics than PCA for most of the data sets in this work and that the regressor(Support Vector Regression,SVR)performs better with the t-SNE reduced features as inputs,accomplishing better predictions with lower error rates.Comparing the results with current literature,our shallow learning model paired with t-SNE achieves either better or on par results than those reported,even competing with more advanced models that use deep learning techniques,which should propel the introduction of t-SNE in more studies that require dimensionality reduction.

关 键 词：Hyperspectral images Dimensionality reduction Regression T-SNE Support vector machines Wine grape berries 

分 类 号：O17[理学—数学]