2021/09/05 by Deepak Sharma, Sharma, Deepak, Bijendra Kumar +3
Computer Science · Mathematics · Social Sciences · #Artificial intelligence #Computational and Text Analysis Methods #Computer science #Curse of dimensionality #Data mining #Data science #Expert finding and Q&A systems #FOS: Computer and information sciences #Field (mathematics) #H.4 #Hellinger distance #I.7 #Information Retrieval (cs.IR) #Information retrieval #Machine learning #Mathematics #Similarity (geometry) #Statistics #Topic Modeling #cs.IR #msc:H.4 #msc:I.7
paper · pdf · doi:10.48550/arxiv.2109.02022
12 Pages,6 Figures, Presented in conference
arxiv created 2021/09/05 · openalex publication_date 2021/09/05 · arxiv updated 2021/09/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The aim of this paper is to uncover the researchers in machine learning using the author-topic model (ATM). We collect 16,855 scientific papers from six top journals in the field of machine learning published from 1997 to 2016 and analyze them using ATM. The dataset is broken down into 4 intervals to identify the top researchers and find similar researchers using their similarity score. The similarity score is calculated using Hellinger distance. The researchers are plotted using t-SNE, which reduces the dimensionality of the data while keeping the same distance between the points. The analysis of our study helps the upcoming researchers to find the top researchers in their area of interest.