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Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
Data Representation Nonnegtive Matrix Factorization Graph Regularization Multiple Kernel Learning.
2012/9/18
Nonnegative Matrix Factorization (NMF) has been contin-uously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank ...
Distance Metric Learning for Kernel Machines
metric learning distance learning support vector machines semi-de nite programming Mahalanobis distance
2012/9/17
Recent work in metric learning has signi cantly improved the state-of-the-art ink-nearest neighbor classi cation. Support vector machines (SVM), particularly with RBF kernels, are amongst the most pop...
Path Integral Control by Reproducing Kernel Hilbert Space Embedding
Path Integral Control Reproducing Kernel Hilbert Space Embedding
2012/9/18
We present an embedding of stochastic optimal control problems, of the so called path integral form, into reproducing kernel Hilbert spaces. Using consistent, sample based estimates of the embedding l...
Sharp analysis of low-rank kernel matrix approximations
Sharp analysis low-rank kernel matrix approximations
2012/9/18
We consider supervised learning problems within the positive-definite kernel framework,such as kernel ridge regression, kernel logistic regression or the support vector machine. With kernels leading t...
We propose a method for nonparametric density estimation that exhibits robustness to contamination of the training sample. This method achieves robustness by combining a traditional kernel density est...
Functional kernel estimators of large conditional quantiles
Conditional quantiles heavy-tailed distributions functional kernel estimator
2011/7/19
We address the estimation of conditional quantiles when the covariate is functional and when the order of the quantiles converges to one as the sample size increases.
Geometric Allocation Approach for Transition Kernel of Markov Chain
Markov chain Transition kernel Geometric allocation
2011/7/7
We introduce a new geometric approach that constructs a transition kernel of Markov chain. Our method always minimizes the average rejection rate and even reduce it to zero in many relevant cases, whi...
Sequential Monte Carlo (SMC) approaches have become work horses in approximate Bayesian computation (ABC). Here we discuss how to construct the perturbation kernels that are required in ABC-SMC approa...
Online Multiple Kernel Learning for Structured Prediction
Online Multiple Kernel Learning r Structured Prediction
2010/10/19
Despite the recent progress towards efficient multiple kernel learning (MKL), the structured output case remains an open research front. Current approaches involve repeatedly solving a batch learning...
Nonparametric kernel estimation of the probability density function of regression errors using estimated residuals
Kernel density estimation Leave-one-out kernel estimator Two-steps estimator
2010/10/14
This paper deals with the nonparametric density estimation of the regression error term assuming its independence with the covariate. The difference between the feasible estimator which uses the estim...
Minimax Robust Function Approximation in Reproduction Kernel Hilbert Spaces
RKHS Thin-Plate Splines Smoothing Splines Scattered Data Interpolation and Approximation
2010/4/30
In this paper, we present a unified approach to function approximation in reproducing kernel Hilbert spaces (RKHS) that establishes a previously unrecognized optimality property for several well-known...
Maxiset in sup-norm for kernel estimators。
Kernel methods and minimum contrast estimators for empirical deconvolution
bandwidth inverse problems kernel estimators local linearmethods local polynomial methods minimum contrast methods
2010/3/11
We survey classical kernel methods for providing nonparametric solutions
to problems involving measurement error. In particular we outline
kernel-basedmethodology in this setting, and discuss its ba...
Classifying Network Data with Deep Kernel Machines
deep architecture diffusion kernel kernel density estimation nearest centroid socialnetwork support vector machine
2010/3/9
Inspired by a growing interest in analyzing network data, we study the problem of node classifi-
cation on graphs, focusing on approaches based on kernel machines. Conventionally, kernel machines
ar...
Minimax properties of beta kernel density estimators
Beta Kernel Density Minimax estimation
2010/3/9
In this paper, we are interested in the study of beta kernel estimators from
an asymptotic minimax point of view. It is well known that beta kernel estimators
are—on the contrary of classical kernel...