We address the problem of partial symmetry detection, i.e., the identification of building blocks a complex shape is composed of. Previous techniques identify parts that relate to each other by simple rigid mappings, similarity transforms, or, more recently, intrinsic isometries. Our approach generalizes the notion of partial symmetries to more general deformations. We introduce subspace symmet...

Independent subspace analysis (ISA) that deals with multidimensional independent sources, is a generalization of independent component analysis (ICA). However, all known ISA algorithms may become ineffective when the sources possess temporal structure. The innovation process instead of the original mixtures has been proposed to solve ICA problems with temporal dependencies. Here we show that th...

In stationary subspace analysis (SSA) one assumes that the observable p-variate time series is a linear mixture of k-variate nonstationary and (p−k)-variate series. The aim then to estimate unmixing matrix which transforms observed multivariate onto components. classical approach data are projected subspaces by minimizing Kullback–Leibler divergence between Gaussian distributions, method only d...

Subspace clustering is the problem of partitioning unlabeled data points into a number of clusters so that data points within one cluster lie approximately on a low-dimensional linear subspace. In many practical scenarios, the dimensionality of data points to be clustered are compressed due to constraints of measurement, computation or privacy. In this paper, we study the theoretical properties...

Non-Gaussian component analysis (NGCA) is aimed at identifying a linear subspace such that the projected data follows a nonGaussian distribution. In this paper, we propose a novel NGCA algorithm based on logdensity gradient estimation. Unlike existing methods, the proposed NGCA algorithm identifies the linear subspace by using the eigenvalue decomposition without any iterative procedures, and t...

We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and compare it against several competing techniques: generalized Fisher discriminant analysis (GDA) and kernel principal components analysis (KPCA) in classification problems. We evaluate the classification performance of the ...