Sandro Vega-Pons

916 total citations · 1 hit paper
10 papers, 531 citations indexed

About

Sandro Vega-Pons is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Sandro Vega-Pons has authored 10 papers receiving a total of 531 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 4 papers in Cognitive Neuroscience. Recurrent topics in Sandro Vega-Pons's work include Advanced Clustering Algorithms Research (5 papers), Face and Expression Recognition (4 papers) and Functional Brain Connectivity Studies (3 papers). Sandro Vega-Pons is often cited by papers focused on Advanced Clustering Algorithms Research (5 papers), Face and Expression Recognition (4 papers) and Functional Brain Connectivity Studies (3 papers). Sandro Vega-Pons collaborates with scholars based in Italy, Austria and Germany. Sandro Vega-Pons's co-authors include José Ruíz-Shulcloper, Paolo Avesani, Seyed Mostafa Kia, Nathan Weisz, Andrea Passerini, Michael Andric, Uri Hasson, Alessandro Gozzi, Luca Dodero and Emanuele Olivetti and has published in prestigious journals such as Pattern Recognition, Neurocomputing and Pattern Recognition Letters.

In The Last Decade

Sandro Vega-Pons

10 papers receiving 503 citations

Hit Papers

align trajectories

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.

A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS

2011 384 citations Sandro Vega-Pons, José Ruíz-Shulcloper International Journal of Pattern Recognition and Artificial Intelligence profile →

Peers

Sandro Vega-Pons

AI

CVPR

SNP

SP

MB

Marco Bressan Italy

Xiaosheng Zhuang Hong Kong

Stergios Papadimitriou Greece

Akshay Gadde United States

Syam Sankar India

Yunzhang Zhu United States

Simone Romano Australia

Linlin Zong China

Yasuo Matsuyama Japan

Marco Bressan Italy

Sandro Vega-Pons

157 ×0.4

AI

263 ×1.4

CVPR

90 ×0.8

SNP

82 ×0.9

SP

25 ×0.4

MB

Citations per year, relative to Sandro Vega-Pons Sandro Vega-Pons (= 1×) peers Marco Bressan

Countries citing papers authored by Sandro Vega-Pons

Since Specialization

Citations

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

Fields of papers citing papers by Sandro Vega-Pons

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sandro Vega-Pons. 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 Sandro Vega-Pons. The network helps show where Sandro Vega-Pons may publish in the future.

Co-authorship network of co-authors of Sandro Vega-Pons

This figure shows the co-authorship network connecting the top 25 collaborators of Sandro Vega-Pons. A scholar is included among the top collaborators of Sandro Vega-Pons 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 Sandro Vega-Pons. Sandro Vega-Pons is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Vega-Pons, Sandro, Emanuele Olivetti, Paolo Avesani, et al.. (2017). Differential Effects of Brain Disorders on Structural and Functional Connectivity. Frontiers in Neuroscience. 10. 605–605. 11 indexed citations

2.

Kia, Seyed Mostafa, Sandro Vega-Pons, Nathan Weisz, & Andrea Passerini. (2017). Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects. Frontiers in Neuroscience. 10. 619–619. 13 indexed citations

3.

Vega-Pons, Sandro & Paolo Avesani. (2014). On pruning the search space for clustering ensemble problems. Neurocomputing. 150. 481–489. 7 indexed citations

4.

Vega-Pons, Sandro, Paolo Avesani, Michael Andric, & Uri Hasson. (2014). Classification of inter-subject fMRI data based on graph kernels. Institutional Research Information System (Università degli Studi di Trento). 12. 1–4. 12 indexed citations

5.

Vega-Pons, Sandro & Paolo Avesani. (2013). Brain Decoding via Graph Kernels. 12. 136–139. 7 indexed citations

6.

Vega-Pons, Sandro, et al.. (2011). Weighted association based methods for the combination of heterogeneous partitions. Pattern Recognition Letters. 32(16). 2163–2170. 16 indexed citations

7.

Vega-Pons, Sandro, Xiaoyi Jiang, & José Ruíz-Shulcloper. (2011). Segmentation Ensemble via Kernels. 3. 686–690. 4 indexed citations

8.

Vega-Pons, Sandro & José Ruíz-Shulcloper. (2011). A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS. International Journal of Pattern Recognition and Artificial Intelligence. 25(3). 337–372. 384 indexed citations breakdown →

9.

Vega-Pons, Sandro, et al.. (2010). Weighted partition consensus via kernels. Pattern Recognition. 43(8). 2712–2724. 69 indexed citations

10.

Vega-Pons, Sandro, et al.. (2008). Active contour algorithm for texture segmentation using a texture feature set. Proceedings - International Conference on Pattern Recognition. 1–4. 8 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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