A Merging Algorithm for Gaussian Mixture Components
نویسندگان
چکیده
منابع مشابه
Methods for merging Gaussian mixture components
The problem of merging Gaussian mixture components is discussed in situations where a Gaussian mixture is fitted but the mixture components are not separated enough from each other to interpret them as “clusters”. The problem of merging Gaussian mixtures is not statistically identifiable, therefore merging algorithms have to be based on subjective cluster concepts. Cluster concepts based on uni...
متن کاملA Novel Merging Algorithm in Gaussian Mixture Probability Hypothesis Density Filter for Close Proximity Targets Tracking ⋆
This paper proposes a novel merging algorithm in Gaussian mixture probability hypothesis density filter to track close proximity targets. The proposed algorithm is added after GM-PHD recursion, in a condition that more than one target has the same state. The weights of Gaussian components decide whether the components can be utilized to extract states, and the means and covariances of Gaussian ...
متن کاملMultiresolution Mixture Modeling using Merging of Mixture Components
Observing natural phenomena at several levels of detail results in multiresolution data. Extending models and algorithms to cope with multiresolution data is a prerequisite for wide-spread exploitation of the data represented in the multiple resolutions. Mixture models are widely used probabilistic models, however, the mixture models in their standard form can be used to analyze the data repres...
متن کاملMerging Mixture Components for Cell Population Identification in Flow Cytometry
We present a framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. We show that the cluster merging algorithm under our framework improves model fit and provides a better estimate of the number of distinct cell subpopulations than either Gaussian mixture models or flowClust, especially for complicated ...
متن کاملA Discrimative Training Algorithm for Gaussian Mixture Speaker Models
The Gaussian mixture speaker model (GMM) is usually trained with the expectation-maximization (EM) algorithm to maximize the likelihood (ML) of observation data from an individual class. The GMM trained based the ML criterion has weak discriminative power when used as a classifier. In this paper, a discriminative training procedure is proposed to fine-tune the parameters in the GMMs. The goal o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2013
ISSN: 1556-5068
DOI: 10.2139/ssrn.2233307