11 research outputs found
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This paper presents a novel quadratic projection based feature extraction
framework, where a set of quadratic matrices is learned to distinguish each
class from all other classes. We formulate quadratic matrix learning (QML) as a
standard semidefinite programming (SDP) problem. However, the con- ventional
interior-point SDP solvers do not scale well to the problem of QML for
high-dimensional data. To solve the scalability of QML, we develop an efficient
algorithm, termed DualQML, based on the Lagrange duality theory, to extract
nonlinear features. To evaluate the feasibility and effectiveness of the
proposed framework, we conduct extensive experiments on biometric recognition.
Experimental results on three representative biometric recogni- tion tasks,
including face, palmprint, and ear recognition, demonstrate the superiority of
the DualQML-based feature extraction algorithm compared to the current
state-of-the-art algorithm
Técnicas baseadas em subespaços e aplicações
Doutoramento em Engenharia ElectrónicaEste trabalho focou-se no estudo de técnicas de sub-espaço tendo em vista as
aplicações seguintes: eliminação de ruído em séries temporais e extracção de
características para problemas de classificação supervisionada. Foram estudadas
as vertentes lineares e não-lineares das referidas técnicas tendo como ponto de
partida os algoritmos SSA e KPCA. No trabalho apresentam-se propostas para
optimizar os algoritmos, bem como uma descrição dos mesmos numa abordagem
diferente daquela que é feita na literatura. Em qualquer das vertentes, linear ou
não-linear, os métodos são apresentados utilizando uma formulação algébrica
consistente. O modelo de subespaço é obtido calculando a decomposição em
valores e vectores próprios das matrizes de kernel ou de correlação/covariância
calculadas com um conjunto de dados multidimensional.
A complexidade das técnicas não lineares de subespaço é discutida,
nomeadamente, o problema da pre-imagem e a decomposição em valores e
vectores próprios de matrizes de dimensão elevada. Diferentes algoritmos de préimagem
são apresentados bem como propostas alternativas para a sua
optimização. A decomposição em vectores próprios da matriz de kernel baseada
em aproximações low-rank da matriz conduz a um algoritmo mais eficiente- o
Greedy KPCA.
Os algoritmos são aplicados a sinais artificiais de modo a estudar a influência dos
vários parâmetros na sua performance. Para além disso, a exploração destas
técnicas é extendida à eliminação de artefactos em séries temporais biomédicas
univariáveis, nomeadamente, sinais EEG.This work focuses on the study of linear and non-linear subspace projective
techniques with two intents: noise elimination and feature extraction. The
conducted study is based on the SSA, and Kernel PCA algorithms.
Several approaches to optimize the algorithms are addressed along with a
description of those algorithms in a distinct approach from the one made in the
literature. All methods presented here follow a consistent algebraic formulation
to manipulate the data. The subspace model is formed using the elements from
the eigendecomposition of kernel or correlation/covariance matrices computed
on multidimensional data sets.
The complexity of non-linear subspace techniques is exploited, namely the preimage
problem and the kernel matrix dimensionality. Different pre-image
algorithms are presented together with alternative proposals to optimize them.
In this work some approximations to the kernel matrix based on its low rank
approximation are discussed and the Greedy KPCA algorithm is introduced.
Throughout this thesis, the algorithms are applied to artificial signals in order to
study the influence of the several parameters in their performance.
Furthermore, the exploitation of these techniques is extended to artefact
removal in univariate biomedical time series, namely, EEG signals.FCT - SFRH/BD/28404/200
Model Order Reduction
An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science
Workshop on Harmonic Oscillators
Proceedings of a workshop on Harmonic Oscillators held at the College Park Campus of the University of Maryland on March 25 - 28, 1992 are presented. The harmonic oscillator formalism is playing an important role in many branches of physics. This is the simplest mathematical device which can connect the basic principle of physics with what is observed in the real world. The harmonic oscillator is the bridge between pure and applied physics