SugiyamaMasashi

10 total papers · 1.0k total citations
8 papers, 855 citations indexed

About

SugiyamaMasashi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistics and Probability. According to data from OpenAlex, SugiyamaMasashi has authored 8 papers receiving a total of 855 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Statistics and Probability. Recurrent topics in SugiyamaMasashi's work include Advanced Statistical Methods and Models (2 papers), Face and Expression Recognition (2 papers) and Fuzzy Logic and Control Systems (1 paper). SugiyamaMasashi is often cited by papers focused on Advanced Statistical Methods and Models (2 papers), Face and Expression Recognition (2 papers) and Fuzzy Logic and Control Systems (1 paper). SugiyamaMasashi collaborates with scholars based in . SugiyamaMasashi's co-authors include and has published in prestigious journals such as Journal of Machine Learning Research and Knowledge and Information Systems.

In The Last Decade

SugiyamaMasashi

8 papers receiving 843 citations

Hit Papers

normalize citations (0→100%) normalize lifespan (0→100%) sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation benchmark for papers published in the same subfield and year, or reaches the top citation threshold in at least one of its specific research topics.

Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis

2007 608 citations SugiyamaMasashi Journal of Machine Learning Research profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
SugiyamaMasashi	445	289	233	117	98	8	855
Kai Zhang	564 1.3×	258 0.9×	173 0.7×	88 0.8×	40 0.4×	14	742
Quanxue Gao	628 1.4×	229 0.8×	206 0.9×	146 1.2×	31 0.3×	30	862
Shiming Xiang	667 1.5×	261 0.9×	364 1.6×	45 0.4×	100 1.0×	14	1.0k
Atalay Barkana	547 1.2×	183 0.6×	115 0.5×	194 1.7×	40 0.4×	33	798
C. H. Chen	390 0.9×	240 0.8×	102 0.4×	56 0.5×	31 0.3×	12	855
Edward W. Wild	606 1.4×	620 2.1×	77 0.3×	82 0.7×	32 0.3×	13	941
Weida Zhou	412 0.9×	340 1.2×	98 0.4×	95 0.8×	43 0.4×	41	813
Chunhong Pan	533 1.2×	219 0.8×	191 0.8×	45 0.4×	57 0.6×	14	711
Francis R. Bach	410 0.9×	318 1.1×	99 0.4×	166 1.4×	13 0.1×	11	823
Warren Koontz	262 0.6×	295 1.0×	78 0.3×	117 1.0×	14 0.1×	18	735

Countries citing papers authored by SugiyamaMasashi

Since Specialization

Citations

This map shows the geographic impact of SugiyamaMasashi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by SugiyamaMasashi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites SugiyamaMasashi more than expected).

Fields of papers citing papers by SugiyamaMasashi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by SugiyamaMasashi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by SugiyamaMasashi. The network helps show where SugiyamaMasashi may publish in the future.

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Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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