This is the second of a two-part paper dealing with the performance of subspace-based algorithms for narrow-hand direction-of-arrival (DOA) estimation when the array manifold and noise covariance are not correctly modeled. In Part I, the performance of the MUSIC algorithm was investigated. In Part 11, we extend this analysis to multidimensional (MD) subspacebased algorithms including determinis...

Abstract: In independent subspace analysis (ISA) one assumes that the components of the observed random vector are linear combinations of the components of a latent random vector with independent subvectors. The problem is then to find an estimate of a transformation matrix to recover the independent subvectors. Regular independent component analysis (ICA) is a special case. In this paper we sh...

In this paper, a novel technique named random subspace two-dimensional LDA (RS-2DLDA) is developed for face recognition. This approach offers a number of improvements over the random subspace two-dimensional PCA (RS2DPCA) framework introduced by Nguyen et al. [5]. Firstly, the eigenvectors from 2DLDA have more discriminative power than those from 2DPCA, resulting in higher accuracy for the RS-2...

In this paper we propose a novel approach for ensemble construction based on the use of nonlinear projections to achieve both accuracy and diversity of individual classifiers. The proposed approach combines the philosophy of boosting, putting more effort on difficult instances, with the basis of the random subspace method. Our main contribution is that instead of using a random subspace, we con...

Random projection has been widely used in data classification. It maps high-dimensional data into a low-dimensional subspace in order to reduce the computational cost in solving the related optimization problem. While previous studies are focused on analyzing the classification performance of using random projection, in this work, we consider the recovery problem, i.e., how to accurately recove...

In this paper, we present a multi-resolution random field model (RFM) and a corresponding algorithm for anomaly subspace detection. We utilize the redundant discrete wavelet transform (RDWT) for generating a multi-resolution feature space, and model each layer by a non-casual RFM with different sets of parameters. A multi-resolution matched subspace detector (MSD) is designed for detecting targ...

We present an approach for the automatic classification of Nuclear Magnetic Resonance Spectroscopy data of biofluids with respect to drug induced organ toxicities. Classification is realized by an Ensemble of Support Vector Machines, trained on different subspaces according to a modified version of Random Subspace Sampling. Features most likely leading to an improved classification accuracy are...

In this paper, we investigate the performance of several systems based on ensemble of classifiers for bankruptcy prediction and credit scoring. The obtained results are very encouraging, our results improved the performance obtained using the stand-alone classifiers. We show that the method ‘‘Random Subspace” outperforms the other ensemble methods tested in this paper. Moreover, the best stand-...

The performance of a single weak classifier can be improved by using combining techniques such as bagging, boosting and the random subspace method. When applying them to linear discriminant analysis, it appears that they are useful in different situations. Their performance is strongly affected by the choice of the base classifier and the training sample size. As well, their usefulness depends ...

The bio-molecular diagnosis of malignancies represents a difficult learning task, because of the high dimensionality and low cardinality of the data. Many supervised learning techniques, among them support vector machines, have been experimented, using also feature selection methods to reduce the dimensionality of the data. In alternative to feature selection methods, we proposed to apply rando...