Saúl Solorio-Fernández

983 total citations · 1 hit paper
8 papers, 662 citations indexed

About

Saúl Solorio-Fernández is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Saúl Solorio-Fernández has authored 8 papers receiving a total of 662 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 4 papers in Media Technology. Recurrent topics in Saúl Solorio-Fernández's work include Face and Expression Recognition (8 papers), Remote-Sensing Image Classification (4 papers) and Machine Learning and Data Classification (3 papers). Saúl Solorio-Fernández is often cited by papers focused on Face and Expression Recognition (8 papers), Remote-Sensing Image Classification (4 papers) and Machine Learning and Data Classification (3 papers). Saúl Solorio-Fernández collaborates with scholars based in Mexico and United States. Saúl Solorio-Fernández's co-authors include José Fco. Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa and Yanqing Zhang and has published in prestigious journals such as Expert Systems with Applications, Pattern Recognition and Neurocomputing.

In The Last Decade

Saúl Solorio-Fernández

8 papers receiving 647 citations

Hit Papers

A review of unsupervised feature selection methods 2019 2026 2021 2023 2019 100 200 300 400
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 threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A review of unsupervised feature selection methods
    2019 417 citations Saúl Solorio-Fernández, Jesús Ariel Carrasco-Ochoa et al. Artificial Intelligence Review profile →

Peers

Saúl Solorio-Fernández
Ritam Guha India
Pablo Bermejo Spain
Ibrahim El-Henawy Egypt
Iffat A. Gheyas United Kingdom
Artur Ferreira Portugal
Xingwang Zhao China
Jan N. van Rijn Netherlands
Geok See Ng Singapore
Zhifeng Hao China
H. Vafaie United States
Ritam Guha India
Saúl Solorio-Fernández
Citations per year, relative to Saúl Solorio-Fernández Saúl Solorio-Fernández (= 1×) peers Ritam Guha

Countries citing papers authored by Saúl Solorio-Fernández

Since Specialization
Citations

This map shows the geographic impact of Saúl Solorio-Fernández'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 Saúl Solorio-Fernández with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saúl Solorio-Fernández more than expected).

Fields of papers citing papers by Saúl Solorio-Fernández

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Saúl Solorio-Fernández. 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 Saúl Solorio-Fernández. The network helps show where Saúl Solorio-Fernández may publish in the future.

Co-authorship network of co-authors of Saúl Solorio-Fernández

This figure shows the co-authorship network connecting the top 25 collaborators of Saúl Solorio-Fernández. A scholar is included among the top collaborators of Saúl Solorio-Fernández based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Saúl Solorio-Fernández. Saúl Solorio-Fernández is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
1.
Solorio-Fernández, Saúl, Jesús Ariel Carrasco-Ochoa, & José Fco. Martínez-Trinidad. (2023). Filter unsupervised spectral feature selection method for mixed data based on a new feature correlation measure. Neurocomputing. 571. 127111–127111. 8 indexed citations
2.
Solorio-Fernández, Saúl, Jesús Ariel Carrasco-Ochoa, & José Fco. Martínez-Trinidad. (2021). A survey on feature selection methods for mixed data. Artificial Intelligence Review. 55(4). 2821–2846. 33 indexed citations
3.
Solorio-Fernández, Saúl, José Fco. Martínez-Trinidad, & Jesús Ariel Carrasco-Ochoa. (2020). A Supervised Filter Feature Selection method for mixed data based on Spectral Feature Selection and Information-theory redundancy analysis. Pattern Recognition Letters. 138. 321–328. 29 indexed citations
4.
Solorio-Fernández, Saúl, Jesús Ariel Carrasco-Ochoa, & José Fco. Martínez-Trinidad. (2020). A systematic evaluation of filter Unsupervised Feature Selection methods. Expert Systems with Applications. 162. 113745–113745. 15 indexed citations
5.
Solorio-Fernández, Saúl, Jesús Ariel Carrasco-Ochoa, & José Fco. Martínez-Trinidad. (2019). A review of unsupervised feature selection methods. Artificial Intelligence Review. 53(2). 907–948. 417 indexed citations breakdown →
6.
Solorio-Fernández, Saúl, José Fco. Martínez-Trinidad, & Jesús Ariel Carrasco-Ochoa. (2017). A new Unsupervised Spectral Feature Selection Method for mixed data: A filter approach. Pattern Recognition. 72. 314–326. 57 indexed citations
7.
Solorio-Fernández, Saúl, Jesús Ariel Carrasco-Ochoa, & José Fco. Martínez-Trinidad. (2016). A new hybrid filter–wrapper feature selection method for clustering based on ranking. Neurocomputing. 214. 866–880. 99 indexed citations
8.
Solorio-Fernández, Saúl, José Fco. Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, & Yanqing Zhang. (2012). Hybrid feature selection method for biomedical datasets. 4 indexed citations

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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