In our earlier work [1, 2], we developed a robust detector for multipath constrained environments when the transmitted signal is known. In this paper, we extend these results to the case where the transmitted signal is a random process. The approach in [1, 2] is to replace the orthogonal projection on the multipath signal subspace S by the orthogonal projection on a representation subspace G, s...

This paper presents a work of utilizing multi-space random mapping (MRM) to formulate a dual-factor identification system, which combines speaker biometric and personal token. Personal token will be assigned to the client to constitute a unique random subspace during enrollment and the speaker template will be generated within the random subspace. Test features will be mapped to the random subs...

Variable screening methods have been shown to be effective in dimension reduction under the ultra-high dimensional setting. Most existing are designed rank predictors according their individual contributions response. As a result, variables that marginally independent but jointly dependent with response could missed. In this work, we propose new framework for variable screening, random subspace...

This paper proposes an improved random forest algorithm for classifying text data. This algorithm is particularly designed for analyzing very high dimensional data with multiple classes whose well-known representative data is text corpus. A novel feature weighting method and tree selection method are developed and synergistically served for making random forest framework well suited to categori...

We consider the problem of image recognition via two-dimensional random projection and nearest constrained subspace. First, image features are extracted by a two-dimensional random projection. The two-dimensional random projection for feature extraction is an extension of the 1D compressive sampling technique to 2D and is computationally more efficient than its 1D counterpart and 2D reconstruct...

We rst develop a notion of quadratic form on a locally compact abelian group. Under suitable hypotheses, we construct a probability measure on the set of closed maximal isotropic subspaces of a locally compact quadratic space over Fp. A random subspace chosen with respect to this measure is discrete with probability 1, and the dimension of its intersection with a xed compact open maximal isotro...

The technique of random projection is one of dimension reduction, where high dimensional vectors in RD are projected down to a smaller subspace in Rk. Certain forms of distances or distance kernels such as Euclidean distances, inner products [10], and lp distances [12] between high dimensional vectors are approximately preserved in this smaller dimensional subspace. Word vectors which are repre...