We propose a new ensemble classification algorithm, named super random subspace (Super RaSE), to tackle the sparse problem. The proposed algorithm is motivated by (RaSE). RaSE method was shown be flexible framework that can coupled with any existing base classification. However, success of largely depends on proper choice classifier, which unfortunately unknown us. In this work, we show Super a...

Linear Discriminant Analysis (LDA) often suffers from the small sample size problem when dealing with high dimensional face data. Random subspace can effectively solve this problem by random sampling on face features. However, it remains a problem how to construct an optimal random subspace for discriminant analysis and perform the most efficient discriminant analysis on the constructed random ...

Subspace clustering groups data into several lowrank subspaces. In this paper, we propose a theoretical framework to analyze a popular optimization-based algorithm, Sparse Subspace Clustering (SSC), when the data dimension is compressed via some random projection algorithms. We show SSC provably succeeds if the random projection is a subspace embedding, which includes random Gaussian projection...

In this work we present a novel approach to ensemble learning for regression models, by combining the ensemble generation technique of random subspace method with the ensemble integration methods of Stacked Regression and Dynamic Selection. We show that for simple regression methods such as global linear regression and nearest neighbours, this is a more effective method than the popular ensembl...

We report on our recent progress in developing an ensemble of classifiers based algorithm for addressing the missing feature problem. Inspired in part by the random subspace method, and in part by an AdaBoost type distribution update rule for creating a sequence of classifiers, the proposed algorithm generates an ensemble of classifiers, each trained on a different subset of the available featu...

A randomsubsetmethod (RSM)with a newweighting scheme is proposed and investigated for linear regression with a large number of features. Weights of variables are defined as averages of squared values of pertaining t-statistics over fitted models with randomly chosen features. It is argued that such weighting is advisable as it incorporates two factors: a measure of importance of the variable wi...