izpis_h1_title_alt

Approximate multiple kernel learning with least-angle regression
Stražar, Martin (Author), Curk, Tomaž (Author)

.pdfPDF - Presentation file, Download (1,77 MB)
MD5: AB5D1179451FAEB7E5E0E1BE7750DC4A
URLURL - Source URL, Visit https://www.sciencedirect.com/science/article/pii/S0925231219302449 This link opens in a new window

Abstract
Kernel methods provide a principled way for general data representations. Multiple kernel learning and kernel approximation are often treated as separate tasks, with considerable savings in time and memory expected if the two are performed simultaneously. Our proposed Mklaren algorithm selectively approximates multiple kernel matrices in regression. It uses Incomplete Cholesky Decomposition and Least-angle regression (LAR) to select basis functions, achieving linear complexity both in the number of data points and kernels. Since it approximates kernel matrices rather than functions, it allows to combine an arbitrary set of kernels. Compared to single kernel-based approximations, it selectively approximates different kernels in different regions of the input spaces. The LAR criterion provides a robust selection of inducing points in noisy settings, and an accurate modelling of regression functions in continuous and discrete input spaces. Among general kernel matrix decompositions, Mklaren achieves minimal approximation rank required for performance comparable to using the exact kernel matrix, at a cost lower than 1% of required operations. Finally, we demonstrate the scalability and interpretability in settings with millions of data points and thousands of kernels.

Language:English
Keywords:kernel methods, kernel approximation, multiple kernel learning, least-angle regression
Work type:Article (dk_c)
Tipology:1.01 - Original Scientific Article
Organization:FRI - Faculty of computer and information science
Year:2019
Number of pages:Str. 245-258
Numbering:Vol. 340
UDC:004
ISSN on article:0925-2312
DOI:10.1016/j.neucom.2019.02.030 This link opens in a new window
COBISS.SI-ID:1538162883 This link opens in a new window
Views:84
Downloads:54
Metadata:XML RDF-CHPDL DC-XML DC-RDF
 
Average score:(0 votes)
Your score:Voting is allowed only to logged in users.
:
Share:AddThis
AddThis uses cookies that require your consent. Edit consent...

Record is a part of a journal

Title:Neurocomputing
Shortened title:Neurocomputing
Publisher:Elsevier
ISSN:0925-2312
COBISS.SI-ID:172315 This link opens in a new window

Document is financed by a project

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:P2-0209
Name:Umetna inteligenca in inteligentni sistemi

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:J7-5460
Name:Post-transkripcijske regulacijske mreže v nevrodegenerativnih boleznih.

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:J1-8150
Name:

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:J3-9263
Name:Vloga paraspeklom podobnih jedrnih telesc pri patogenezi nevrodegenerativnih bolezni ALS in FTD

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:25.03.2021

Secondary language

Language:Slovenian
Keywords:jedrne metode, aproksimacija jeder, učenje z več jedrnimi funkcijami, regresija najmanjših kotov

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Comments

Leave comment

You have to log in to leave a comment.

Comments (0)
0 - 0 / 0
 
There are no comments!

Back