In this paper, we propose an integration of random subspace sampling and Fishervoice for speaker verification. In the previous random sampling framework [1], we randomly sample the JFA feature space into a set of low-dimensional subspaces. For every random subspace, we use Fishervoice to model the intrinsic vocal characteristics in a discriminant subspace. The complex speaker characteristics ar...

The random subspace and the projection methods are investigated compared as techniques for forming ensembles of nearest neighbor classifiers in high dimensional feature spaces. two have been empirically evaluated on three types high-dimensional datasets: microarrays, chemoinformatics, images. Experimental results 34 datasets show that both method lead to improvements predictive performance usin...

• Random Sampling: X are iid uniform from unit sphere in S`. • Random Subspace: S` are spanned by d iid uniform vectors in R. GRAPH CONNECTIVITY What about the second design objective? • Nashihatkon & Hartley nailed that connectivity is NOT a generic property for SSC in general when d ≥ 4. • What about for LRR? Assume random sampling and random subspace, we have: Proposition 1 Under independent...

In practical applications of pattern recognition and computer vision, the performance of many approaches can be improved by using multiple models. In this paper, we develop a common theoretical framework for multiple model fusion at the feature level using multilinear subspace analysis (also known as tensor algebra). One disadvantage of the multilinear approach is that it is hard to obtain enou...

In this paper, we introduce subspace-frequently hypercyclic operators. We show that these operators are subspace-hypercyclic and there are subspace-hypercyclic  operators that are not subspace-frequently hypercyclic. There is a criterion like to subspace-hypercyclicity criterion that implies subspace-frequent hypercyclicity and if an operator $T$ satisfies this criterion, then $Toplus T$ is sub...

Bagging, boosting and random subspace methods are well known re-sampling ensemble methods that generate and combine a diversity of learners using the same learning algorithm for the base-regressor. In this work, we built an ensemble of bagging, boosting and random subspace methods ensembles with 8 sub-regressors in each one and then an averaging methodology is used for the final prediction. We ...

This paper purposes a new method for selecting the most discriminant rotation invariant patterns in local binary patterns and local ternary patterns. Our experiments show that a selection based on variance performs better than the recently proposed method of using dominant local binary patterns (DLBP). Our method uses a random subspace of patterns with higher variance. Features are transformed ...