The complexity of the human brain gives illusion that activity is intrinsically high-dimensional. Nonlinear dimensionality-reduction methods such as uniform manifold approximation and t-distributed stochastic neighbor embedding have been used for high-throughput biomedical data. However, they not extensively data those from functional magnetic resonance imaging (fMRI), primarily due to their in...

The intrinsic nature of noisy and complex data sets is often concealed in low-dimensional structures embedded a higher dimensional space. Number methodologies have been developed to extract represent such the form manifolds (i.e. geometric that locally resemble continuously deformable intervals Rj1). Usually a-priori knowledge manifold's dimensionality required. Additionally, their performance ...

Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries...

Article history: Received 8 September 2008 Received in revised form 23 February 2009 Accepted 1 May 2009

In this paper, two significant weaknesses of locally linear embedding (LLE) applied to computer vision are addressed: ‘‘intrinsic dimension’’ and ‘‘eigenvector meanings’’. ‘‘Topological embedding’’ and ‘‘multi-resolution nonlinearity capture’’ are introduced based on mathematical analysis of topological manifolds and LLE. The manifold topological analysis (MTA) method is described and is based ...

The standard approach to multi-modal registration is to apply sophisticated similarity metrics such as mutual information. The disadvantage of these measures, in contrast to simple L1 or L2 norm, is the increased computational complexity and consequently the prolongation of the registration time. An alternative approach, which has so far not yet gained much attention in the literature, is to fi...