CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

Enhanced Distance Subset Approximation using Class-Specific Subspace Kernel Representation for Kernel Approximation

Yinan Yu (Institutionen för signaler och system, Signalbehandling) ; Konstantinos Diamantaras ; Tomas McKelvey (Institutionen för signaler och system, Signalbehandling) ; S.Y. Kung
Proceeding of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016), Vietri sul Mare, Italy (2161-0363). Vol. 2016-November (2016),
[Konferensbidrag, refereegranskat]

The computational complexity of kernel methods grows at least quadratically with respect to the training size and hence low rank kernel approximation techniques are commonly used. One of the most popular approximations is constructed by sub-sampling the training data. In this paper, we present a sampling algorithm called Enhanced Distance Subset Approximation (EDSA) based on a novel kernel function called CLAss-Specific Kernel (CLASK), which applies the idea of subspace clustering to low rank kernel approximation. By representing the kernel matrix based on a class-specific subspace model, it is allowed to use distinct kernel functions for different classes, which provides a better flexibility compared to classical kernel approximation techniques. Experimental results conducted on various UCI datasets are provided in order to verify the proposed techniques.

Nyckelord: class-specific subspace model; classification; discriminative representation; Kernel approximation


Article number 7738811



Den här publikationen ingår i följande styrkeområden:

Läs mer om Chalmers styrkeområden  

Denna post skapades 2016-10-17. Senast ändrad 2017-02-22.
CPL Pubid: 243540

 

Läs direkt!


Länk till annan sajt (kan kräva inloggning)


Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling (1900-2017)

Ämnesområden

Informations- och kommunikationsteknik
Informationsbehandling
Datorseende och robotik (autonoma system)
Informatik, data- och systemvetenskap
Informationsteknik
Signalbehandling

Chalmers infrastruktur