NDLI: On One-Shot Similarity Kernels: Explicit Feature Maps and Properties

Content Provider	IEEE Xplore Digital Library
Author	Zafeiriou, S. Kotsia, I.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput., Imperial Coll. London, London, UK (Zafeiriou, S.) \|\| Dept. of Phys., Univ. of Patras, Patras, Greece (Kotsia, I.)
Abstract	Kernels have been a common tool of machine learning and computer vision applications for modeling non-linearities and/or the design of robust Robustness may refer to either the presence of outliers and noise or to the robustness to a class of transformations (e.g., translation). similarity measures between objects. Arguably, the class of positive semi-definite (psd) kernels, widely known as Mercer's Kernels, constitutes one of the most well-studied cases. For every psd kernel there exists an associated feature map to an arbitrary dimensional Hilbert space mathcal H, the so-called feature space. The main reason behind psd kernels' popularity is the fact that classification/regression techniques (such as Support Vector Machines (SVMs)) and component analysis algorithms (such as Kernel Principal Component Analysis (KPCA)) can be devised in mathcal H, without an explicit definition of the feature map, only by using the kernel (the so-called kernel trick). Recently, due to the development of very efficient solutions for large scale linear SVMs and for incremental linear component analysis, the research towards finding feature map approximations for classes of kernels has attracted significant interest. In this paper, we attempt the derivation of explicit feature maps of a recently proposed class of kernels, the so-called one-shot similarity kernels. We show that for this class of kernels either there exists an explicit representation in feature space or the kernel can be expressed in such a form that allows for exact incremental learning. We theoretically explore the properties of these kernels and show how these kernels can be used for the development of robust visual tracking, recognition and deformable fitting algorithms.
Sponsorship	IEEE Comput. Soc.
Starting Page	2392
Ending Page	2399
File Size	334537
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.297
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Face Approximation methods Principal component analysis Support vector machines Approximation algorithms Face recognition face tracking face recognition kernel learning
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

On one-shot similarity kernels: explicit feature maps and properties.

Efficient additive kernels via explicit feature maps

Kernel Fukunaga-Koontz Transform Subspaces For Enhanced Face Recognition

Enforcing non-positive weights for stable support vector tracking

The One-Shot similarity kernel

Efficient Additive Kernels via Explicit Feature Maps

Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels

Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels

Face Gender Recognition Based on 2D Principal Component Analysis and Support Vector Machine

On One-Shot Similarity Kernels: Explicit Feature Maps and Properties

Similar Documents

On one-shot similarity kernels: explicit feature maps and properties.

Efficient additive kernels via explicit feature maps

Kernel Fukunaga-Koontz Transform Subspaces For Enhanced Face Recognition

Enforcing non-positive weights for stable support vector tracking

The One-Shot similarity kernel

Efficient Additive Kernels via Explicit Feature Maps

Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels

Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels

Face Gender Recognition Based on 2D Principal Component Analysis and Support Vector Machine

On One-Shot Similarity Kernels: Explicit Feature Maps and Properties