Subspace Ensemble Research Articles

Applications of information theory in ensemble data assimilation

AbstractWe apply information theory within an ensemble‐based data assimilation approach and define information matrix in ensemble subspace. The information matrix in ensemble subspace employs a flow‐dependent forecast error covariance and it is of relatively small dimensions (equal to the ensemble size). The information matrix in ensemble subspace can be directly linked to the information matrix typically used in non‐ensemble‐based data assimilation methods, such as the Kalman Filter (KF) and the three‐dimensional variational (3D‐Var) methods, which provides a framework for consistent comparisons of information measures between different data assimilation methods.We evaluate information measures, such as degrees of freedom for signal, within the Maximum Likelihood Ensemble Filter (MLEF) data assimilation approach and compare them with those obtained using the KF approach and the 3D‐Var approach. We assimilate model‐simulated observations and use the Goddard Earth Observing System Single Column Model (GEOS‐5 SCM) as a dynamical forecast model.The experimental results demonstrate that the proposed framework is useful for comparing information measures obtained in different data assimilation approaches. These comparisons indicate that using a flow‐dependent forecast error covariance matrix (e.g. as in the KF and the MLEF experiments) is fundamentally important for adequately describing prior knowledge about the true model state when calculating information measures of assimilated observations. We also demonstrate that data assimilation results obtained using the KF and the MLEF approach (when ensemble size is larger than 10 ensemble members) are superior to the results of the 3D‐Var approach. Copyright © 2007 Royal Meteorological Society

Subspace ensembles for classification

Ensemble learning constitutes one of the principal current directions in machine learning and data mining. In this paper, we explore subspace ensembles for classification by manipulating different feature subspaces. Commencing with the nature of ensemble efficacy, we probe into the microcosmic meaning of ensemble diversity, and propose to use region partitioning and region weighting to implement effective subspace ensembles. Individual classifiers possessing eminent performance on a partitioned region reflected by high neighborhood accuracies are deemed to contribute largely to this region, and are assigned large weights in determining the labels of instances in this area. A robust algorithm “Sena” that incarnates the mechanism is presented, which is insensitive to the number of nearest neighbors chosen to calculate neighborhood accuracies. The algorithm exhibits improved performance over the well-known ensembles of bagging, AdaBoost and random subspace. The difference of its effectivity with varying base classifiers is also investigated.

Adaptive observations in ensemble data assimilation

An important question in ensemble based data assimilation scheme is how to configure our observations to correctly capture the important features in either our atmospheric or oceanic models given a set of ensembles. In this paper a systematic approach for effective sensor placement is formulated to evaluate how to target our observations. This method is based on a criterion of Shannon information entropy and condition number of our ensemble subspace covariance matrix yielding adaptive observation configurations. The theory behind this method is presented as well as an example illustrated with a global shallow water equations model on the sphere.

An advanced multi-modal method for human authentication featuring biometrics data and tokenised random numbers

In this work, we propose a multi-modal method that combines the scores of selected fingerprint matchers with the scores obtained by a Face Authenticator where the facial features are combined with pseudo-random numbers. We propose a novel method to combine the scores of fingerprint matchers based on random subspace Ensemble and we test the method on the systems submitted to FVC2004. Moreover, we show that methods based on tokenised pseudo-random numbers and user specific biometric features are highly dependent upon a parameter, the hashing threshold; we demonstrate that using an ensemble of classifiers it is possible to solve this problem leading to a considerable performance improvement. Finally, we study the fusion among the scores obtained by a Face Authenticator (where the face features are combined with pseudo-random numbers) and the scores of the systems submitted to FVC2004.

Bio-molecular cancer prediction with random subspace ensembles of support vector machines

Support vector machines (SVMs), and other supervised learning techniques have been experimented for the bio-molecular diagnosis of malignancies, using also feature selection methods. The classification task is particularly difficult because of the high dimensionality and low cardinality of gene expression data. In this paper we investigate a different approach based on random subspace ensembles of SVMs: a set of base learners is trained and aggregated using subsets of features randomly drawn from the available DNA microarray data. Experimental results on the colon adenocarcinoma diagnosis and medulloblastoma clinical outcome prediction show the effectiveness of the proposed approach.