ICASSP
2011
IEEE
13 years 4 months ago
2011
IEEE
Kernel nonnegative matrix factorization (KNMF) is a recent kernel extension of NMF, where matrix factorization is carried out in a reproducing kernel Hilbert space (RKHS) with a f...
JMLR
2010
13 years 7 months ago
2010
Universal kernels have been shown to play an important role in the achievability of the Bayes risk by many kernel-based algorithms that include binary classification, regression, ...
JMLR
2010
13 years 7 months ago
2010
This paper deals with functional regression, in which the input attributes as well as the response are functions. To deal with this problem, we develop a functional reproducing ke...
JMLR
2010
13 years 7 months ago
2010
We prove the statistical consistency of kernel Partial Least Squares Regression applied to a bounded regression learning problem on a reproducing kernel Hilbert space. Partial Lea...
NN
2008
Springer
13 years 11 months ago
2008
Springer
We propose a novel similarity measure, called the correntropy coefficient, sensitive to higher order moments of the signal statistics based on a similarity function called the cro...
IEICET
2010
13 years 11 months ago
2010
Let V (φ) be a shift invariant subspace of L2 (R) generated by a Riesz or frame generator φ(t) in L2 (R). We assume that φ(t) is suitably chosen so that V (φ) becomes a reprod...
JMLR
2002
14 years 3 days ago
2002
We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On th...
PRL
2008
14 years 11 days ago
2008
This paper proposes a new approach for classifying multivariate time-series with applications to the problem of writer independent online handwritten character recognition. Each t...
JMLR
2006
14 years 12 days ago
2006
We determine the asymptotic behaviour of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function i...
FOCM
2006
14 years 13 days ago
2006
This paper considers the regularized learning algorithm associated with the leastsquare loss and reproducing kernel Hilbert spaces. The target is the error analysis for the regres...