Augmenting machine learning photometric redshifts with Gaussian mixture models

METAPHOR: Probability density estimation for machine learning based photometric redshifts

We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any...

Incremental learning with Gaussian mixture models

In this paper we propose a new incremental estimation of Gaussian mixture models which can be used for applications of online learning. Our approach allows for adding new samples incrementally as well as removing parts of the mixture by the process of unlearning. Low complexity of the mixtures is maintained through a novel compression algorithm. In contrast to the existing approaches, our appro...

Efficient Greedy Learning of Gaussian Mixture Models

This article concerns the greedy learning of gaussian mixtures. In the greedy approach, mixture components are inserted into the mixture one after the other. We propose a heuristic for searching for the optimal component to insert. In a randomized manner, a set of candidate new components is generated. For each of these candidates, we find the locally optimal new component and insert it into th...

Efficient greedy learning of Gaussian mixture models

Incremental Learning of Gaussian Mixture Models

Gaussian Mixture Modeling (GMM) is a parametric method for high dimensional density estimation. Incremental learning of GMM is very important in problems such as clustering of streaming data and robot localization in dynamic environments. Traditional GMM estimation algorithms like EM Clustering tend to be computationally very intensive in these scenarios. We present an incremental GMM estimatio...