Comptes Rendus
no. 11
Code2vect: An efficient heterogenous data classifier and nonlinear regression technique
Clara Argerich Martín1 ; Ruben Ibáñez Pinillo1 ; Anais Barasinski2 ; Francisco Chinesta3 
1 PIMM, Arts et Métiers Institute of Technology, CNRS, CNAM, HESAM University, 151, boulevard de l'Hôpital, 75013 Paris, France
2 University of Pau & Pays Adour, E2S UPPA, IPREM UMR5254, 64000 Pau, France
3 ESI GROUP Chair @ PIMM, Arts et Métiers Institute of Technology, 151, boulevard de l'Hôpital, 75013 Paris, France
Comptes Rendus. Mécanique, Volume 347 (2019) no. 11, pp. 754-761.

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.

Reçu le :
Accepté le :
Publié le :
DOI : 10.1016/j.crme.2019.11.002
Mots clés : Machine learning, Data representation, Classification, Categorial data, Neural network, High-dimensional data, Regression
Clara Argerich Martín 1 ; Ruben Ibáñez Pinillo 1 ; Anais Barasinski 2 ; Francisco Chinesta 3

1 PIMM, Arts et Métiers Institute of Technology, CNRS, CNAM, HESAM University, 151, boulevard de l'Hôpital, 75013 Paris, France
2 University of Pau & Pays Adour, E2S UPPA, IPREM UMR5254, 64000 Pau, France
3 ESI GROUP Chair @ PIMM, Arts et Métiers Institute of Technology, 151, boulevard de l'Hôpital, 75013 Paris, France 
@article{CRMECA_2019__347_11_754_0,
     author = {Clara Argerich Mart{\'\i}n and Ruben Ib\'a\~nez Pinillo and Anais Barasinski and Francisco Chinesta},
     title = {\protect\emph{Code2vect}: {An} efficient heterogenous data classifier and nonlinear regression technique},
     journal = {Comptes Rendus. M\'ecanique},
     pages = {754--761},
     publisher = {Elsevier},
     volume = {347},
     number = {11},
     year = {2019},
     doi = {10.1016/j.crme.2019.11.002},
     language = {en},
}
TY  - JOUR
AU  - Clara Argerich Martín
AU  - Ruben Ibáñez Pinillo
AU  - Anais Barasinski
AU  - Francisco Chinesta
TI  - Code2vect: An efficient heterogenous data classifier and nonlinear regression technique
JO  - Comptes Rendus. Mécanique
PY  - 2019
SP  - 754
EP  - 761
VL  - 347
IS  - 11
PB  - Elsevier
DO  - 10.1016/j.crme.2019.11.002
LA  - en
ID  - CRMECA_2019__347_11_754_0
ER  - 
%0 Journal Article
%A Clara Argerich Martín
%A Ruben Ibáñez Pinillo
%A Anais Barasinski
%A Francisco Chinesta
%T Code2vect: An efficient heterogenous data classifier and nonlinear regression technique
%J Comptes Rendus. Mécanique
%D 2019
%P 754-761
%V 347
%N 11
%I Elsevier
%R 10.1016/j.crme.2019.11.002
%G en
%F CRMECA_2019__347_11_754_0
Clara Argerich Martín; Ruben Ibáñez Pinillo; Anais Barasinski; Francisco Chinesta. Code2vect: An efficient heterogenous data classifier and nonlinear regression technique. Comptes Rendus. Mécanique, Volume 347 (2019) no. 11, pp. 754-761. doi : 10.1016/j.crme.2019.11.002. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2019.11.002/

[1] S. Roweis; L. Saul Nonlinear dimensionality reduction by locally linear embedding, Science, Volume 290 (2000) no. 5500, pp. 2323-2326

[2] E. Lopez; D. Gonzalez; J.V. Aguado; E. Abisset-Chavanne; E. Cueto; C. Binetruy; F. Chinesta Archives of computational methods in engineering, Int. J. Numer. Methods Eng., Volume 25 ( January 2018 ) no. 1, pp. 59-68 | DOI

[3] L. Vans Der Maaten; G. Hinton Visualizing data using t-SNE, J. Mach. Learn. Res., Volume 9 (2008), pp. 2579-2605

[4] A. Criminisi; J. Shotton; E. Konukoglu Decision Forests for Classification, Regression, Density Estimation, 2011 (Manifold Learning and Semi-Supervised Learning. Microsoft Research technical report, TR-2011-114)

[5] J. Greenhalgh; M. Miermehdi Traffic sign recognition using Mser and Random forests, 20th European Signal Processing Conference, 2012

[6] J. Schmidhuber Deep learning in neural networks: an overview, Neural Netw., Volume 61 (2015), pp. 85-117

[7] L. Yann; B. Yoshua; H. Geoffrey Deep learning, Nature, Volume 521 (2015), pp. 436-444

[8] J. Donahue; L.A. Hendricks; M. Rohrbach; S. Venugopalan; S. Guadarrama; K. Saenko; T. Darrell Long-term recurrent convolutional networks for visual recognition and description, IEEE Trans. Pattern Anal. Mach. Intell., Volume 39 (2017) no. 4, pp. 677-691

[9] C. Dong; C.C. Loy; K. He; X. Tang Image super-resolution using deep convolutional networks, IEEE Trans. Pattern Anal. Mach. Intell., Volume 38 (2016) no. 2, pp. 295-307

[10] Y. Bengio; R. Ducharme; P. Vincent; C. Jauvin A neural probabilistic language model, J. Mach. Learn. Res., Volume 3 (2003), pp. 1137-1155

[11] P. Norvig; S. Russell Artificial Intelligence, a Modern Approach, Prentice-Hall, 1994

[12] T. Mikolov; I. Sutskever; K. Chen; G. Corrado; J. Dean Distributed representation of words and phrases their compositionality, Proceeding NIPS'13 Proceedings of the 26th International Conference on Neural Information Processing Systems, vol. 2, 2013

[13] M. Abadi et al. Google Research Large-Scale Machine Learning on Heterogeneous Distributed Systems, 2015 (Technical Report)

[14] R. Ibanez; E. Abbisset-Chavanne; A. Ammar; D. Gonzalze; E. Cueto; J.-L. Duval; F. Chinesta A multidimensional data-driven sparse identification technique: the sparse proper generalized decomposition, Complexity (2018) | DOI

[15] G. Quaranta; C. Argerich; R. Ibanez; J.-L. Duval; E. Cueto; F. Chinesta From linear to nonlinear PGD-based parametric structural dynamics, C. R. Mécanique, Volume 347 (2019), pp. 445-454

Cité par Sources :

Commentaires - Politique