Sparse Vector Modeling
Sparse Vector Modeling is a vector modeling approach that represents data using vectors in which most elements are zero, thereby emphasizing computational efficiency and feature selectivity in high-dimensional spaces.
35
Publications
2.7K
Citations
95
Authors
49
Institutions
Publications per year
2017–2026
8
Parents
35
Group lasso with overlap and graph lasso
Laurent Jacob, Guillaume Obozinski, Jean‐Philippe Vert · 2009 · 868 citations
Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection
Ivan Firdausi, Charles Lim, Alva Erwin et al. · 2010 · 326 citations
Peak alignment using reduced set mapping
Ralf J. O. Torgrip, Magnus Åberg, Bo Karlberg et al. · Journal of Chemometrics · 2003 · 97 citations
1–5 of 35
95
Rutgers, The State University of New Jersey, United States
2 publications · 91 citations
2
h-index
2
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2
h-index
Laboratoire de Génie Électrique de Grenoble, France
2 publications · 151 citations
2
h-index
2
h-index
1–5 of 95
49
Électricité de France (France)
Paris, France
3 h-index
9 publications
304 citations
Columbia University
New York, United States
3 h-index
8 publications
254 citations
Technical University of Munich
Munich, Germany
2 h-index
5 publications
82 citations
Princeton University
Princeton, United States
2 h-index
2 publications
91 citations
Tsinghua University
Beijing, China
2 h-index
4 publications
94 citations

Drexel University
Philadelphia, United States
2 h-index
4 publications
182 citations
Georgia Institute of Technology
Atlanta, United States
2 h-index
4 publications
238 citations

Rice University
Houston, United States
2 h-index
6 publications
186 citations
1–8 of 49
8 publications · 436 citations
8
h-index
3 publications · 61 citations
3
h-index
2 publications · 62 citations
2
h-index
1 publications · 97 citations
1
h-index
1 publications · 34 citations
1
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