Unsupervised pseudoinverse hashing learning model for rare astronomical object retrieval  

作　　者：WANG Ke GUO Ping LUO ALi XU MingLiang 

机构地区：[1]Artificial Intelligence Research Center,School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China [2]School of Systems Science,Beijing Normal University,Beijing 100875,China [3]Key Laboratory of Optical Astronomy,National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100012,China

出　　处：《Science China(Technological Sciences)》2022年第6期1338-1348,共11页中国科学（技术科学英文版）

基　　金：supported by the Postdoctoral Science Foundation of China(Grant No.2020M682348);the Key Research Foundation of Henan Higher Education Institutions(Grant No.21A520002);the National Key Research and Development Program of China(Grant No.2018AAA0100203);the Joint Research Fund in Astronomy(Grant No.U1531242)under a cooperative agreement between the National Natural Science Foundation of China and the Chinese Academy of Sciences(CAS)。

摘　　要：Searching for rare astronomical objects based on spectral data is similar to finding needles in a haystack owing to their rarity and the immense data volume gathered from large astronomical spectroscopic surveys.In this paper,we propose a novel automated approximate nearest neighbor search method based on unsupervised hashing learning for rare spectra retrieval.The proposed method employs a multilayer neural network using autoencoders as the local compact feature extractors.Autoencoders are trained with a non-gradient learning algorithm with graph Laplace regularization.This algorithm also simplifies the tuning of network architecture hyperparameters and the learning control hyperparameters.Meanwhile,the graph Laplace regularization can enhance the robustness by reducing the sensibility to noise.The proposed model is data-driven;thus,it can be viewed as a general-purpose retrieval model.The proposed model is evaluated in experiments and real-world applications where rare Otype stars and their subclass are retrieved from the dataset obtained from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope(Guo Shoujing Telescope).The experimental and application results show that the proposed model outperformed the baseline methods,demonstrating the effectiveness of the proposed method in rare spectra retrieval tasks.

关 键 词：compact features unsupervised hashing object retrieval pseudoinverse learning 

分 类 号：P14[天文地球—天体物理] TP391.3[天文地球—天文学]