nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

Manifold Learning for Innovation Funding: Identification of Potential Funding Recipients

verfasst von : Vincent Grollemund, Gaétan Le Chat, Jean-François Pradat-Peyre, François Delbot

Erschienen in: Artificial Intelligence Applications and Innovations

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

finElink is a recommendation system that provides guidance to French innovative companies with regard to their financing strategy through public funding mechanisms. Analysis of financial data from former funding recipients partially feeds the recommendation system. Financial company data from a representative French population are reduced and projected onto a two-dimensional space with Uniform Manifold Approximation and Projection, a manifold learning algorithm. Former French funding recipients’ data are projected onto the two-dimensional space. Their distribution is non-uniform, with data concentrating in one region of the projection space. This region is identified using Density-based Spatial Clustering of Applications with Noise. Applicant companies which are projected within this region are labeled potential funding recipients and will be suggested the most competitive funding mechanisms.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel k-means Cluster Shape Implications

Nächstes Kapitel Network Aggregation to Enhance Results Derived from Multiple Analytics

In geometry, a simplex is defined as a set of points, where none is a barycentre of the remaining points. The convex hull of these points corresponds to the face of the simplex. In simpler terms, a n-simplex can be thought of as the generalization of a triangle in the \(n^{th}\) dimension.

In machine learning, cross entropy is frequently used as a cost function to compare two probability distributions (p,q): p is optimized to approximate q the fixed target distribution.

Agius, R.: Machine learning can identify newly diagnosed patients with CLL at high risk of infection. Nat. Commun. 11(1), 1–17 (2020)CrossRef

Amadeus. https://www.bvdinfo.com/en-gb/our-products/data/international/amadeus. Accessed 25 Feb 2020

Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, no. 34, pp. 226–231 (1996)

finElink. https://www.finelink.eu. Accessed 25 Feb 2020

Fornito, A., Arnatkevičiūtė, A., Fulcher, B.: Bridging the gap between connectome and transcriptome. Trends Cogn. Sci. 23(1), 34–50 (2019) CrossRef

Grollemund, V., Pradat, P.F., Delbot, F., Le Chat, G., Pradat-Peyre, J.F., Bede, P.: Manifold learning for ALS prognosis: development and validation of a prognosis model. Scientific Reports (submitted manuscript)

Lee, J., Verleysen, M.: Nonlinear Dimensionality Reduction. Springer, Heidelberg (2007). https://doi.org/10.1007/978-0-387-39351-3CrossRefMATH

Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)MATH

McInnes, L., Healy, J., Melville, J.: UMAP: uniform manifold approximation and projection for dimension reduction. arXiv, preprint arXiv:1802.03426 (2018)

10.

Mika, S., Schölkopf, B., Smola, A., Müller, K., Scholz, M., Rätsch, G.: Kernel PCA and de-noising in feature spaces. In: 11th International Conference on Neural Information Processing Systems, pp. 546–542. MIT Press, Denver (1998)

11.

Roweis, S.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRef

12.

Schafer, J.: Analysis of Incomplete Multivariate Data. Chapman and Hall/CRC, London (1997)CrossRef

13.

Schubert, E., Gertz, M.: Intrinsic t-stochastic neighbor embedding for visualization and outlier detection. In: Beecks, C., Borutta, F., Kröger, P., Seidl, T. (eds.) SISAP 2017. LNCS, vol. 10609, pp. 188–203. Springer, Cham (2017)CrossRef

14.

Silva, V., Tenenbaum, J.: Global versus local methods in nonlinear dimensionality reduction. In: Advances in Neural Information Processing System, pp. 721–728 (2003)

15.

Stekhoven, D.J., Bühlmann, P.: MissForest - non-parametric missing value imputation for mixed-type data. Bioinformatics 28(1), 112–118 (2012)CrossRef

16.

Tang, F., Ishwaran, H.: Random forest missing data algorithms. Stat. Anal. Data Min.: ASA Data Sci. J. 10(6), 363–377 (2017)MathSciNetCrossRef

17.

Tenenbaum, J.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRef

18.

Wattenberg, M., Viégas, F., Johnson, I.: How to use t-SNE effectively. Distill 1(10), e2 (2016)CrossRef

Titel: Manifold Learning for Innovation Funding: Identification of Potential Funding Recipients
verfasst von: Vincent Grollemund
Gaétan Le Chat
Jean-François Pradat-Peyre
François Delbot
Verlag: Springer International Publishing
Buch: Artificial Intelligence Applications and Innovations
Print ISBN: 978-3-030-49160-4

Electronic ISBN: 978-3-030-49161-1

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-49161-1_11

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner