Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data
نویسنده
چکیده
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density estimate. We show how to convert the ensemble estimates into a Mercer Kernel, describe the properties of this new kernel function, and give examples of the performance of this kernel on unsupervised clustering of synthetic data and also in the domain of unsupervised multispectral image understanding.
منابع مشابه
Density Estimation with Mercer Kernels
We present a new method for density estimation based on Mercer kernels. The density estimate can be understood as the density induced on a data manifold by a mixture of Gaussians fit in a feature space. As is usual, the feature space and data manifold are defined with any suitable positive-definite kernel function. We modify the standard EM algorithm for mixtures of Gaussians to infer the param...
متن کاملLearning Graphical Models with Mercer Kernels
We present a class of algorithms for learning the structure of graphical models from data. The algorithms are based on a measure known as the kernel generalized variance (KGV), which essentially allows us to treat all variables on an equal footing as Gaussians in a feature space obtained from Mercer kernels. Thus we are able to learn hybrid graphs involving discrete and continuous variables of ...
متن کاملNon-Stationary Spectral Kernels
We propose non-stationary spectral kernels for Gaussian process regression. Wepropose to model the spectral density of a non-stationary kernel function as amixture of input-dependent Gaussian process frequency density surfaces. Wesolve the generalised Fourier transform with such a model, and present a familyof non-stationary and non-monotonic kernels that can learn input-depende...
متن کاملEnsemble Kernel Learning Model for Prediction of Time Series Based on the Support Vector Regression and Meta Heuristic Search
In this paper, a method for predicting time series is presented. Time series prediction is a process which predicted future system values based on information obtained from past and present data points. Time series prediction models are widely used in various fields of engineering, economics, etc. The main purpose of using different models for time series prediction is to make the forecast with...
متن کاملApplication of Adaptive Mixtures and Fractal Dimension Analysis Technique to Particle Physics
The discrimination of physics “signal” from “background” is one of the most important subjects in high energy physics analysis since this process usually governs the magnitude of measurement errors. Background suppression using kernel density estimation to estimate the parent distribution of a data sample appears to be an effective method. In this paper, Adaptive Mixtures [1] and Kernel Density...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2004