Parameter estimation becomes difficult in high-dimensional spaces due to the increasing sparseness of the data. Therefore, when a low-dimensional representation is embedded in the data, dimensionality reduction methods become useful. One such method-projection pursuit regression (Friedman and Stuetzle 1981 (PPR)-is capable of performing dimensionality reduction by composition, namely, it constr...

Tree projections provide a unifying framework to deal with most structural decomposition methods of constraint satisfaction problems (CSPs). Within this framework, a CSP instance is decomposed into a number of sub-problems, called views, whose solutions are either already available or can be computed efficiently. The goal is to arrange portions of these views in a tree-like structure, called tr...

In finite deformation inelasticity, one often has to deal with the incompressibility constraint. past, this was dealt using, for example, an exponential mapping approach, which yields exact volume preservation in plastic deformations. article however, special-linear update approach by Hurtado et al. The update: An application of differential manifold theory isochoric plasticity flow rules, Int ...

In this paper, we propose a new fast and robust recursive algorithm for near-separable nonnegative matrix factorization, a particular nonnegative blind source separation problem. This algorithm, which we refer to as the successive nonnegative projection algorithm (SNPA), is closely related to the popular successive projection algorithm (SPA), but takes advantage of the nonnegativity constraint ...

We analyze Case in terms of independent constraints on syntactic structures — namely, the Projection Principle (inherent Case), the ECP (marked structural Case), and the theory of extended projections (the nominative, a Caseless nominal projection). The resulting theory accounts for (1) the government constraint on Case assignment, (2) all major Case systems (accusative, ergative, active, three...

An approach for tracking the motion of a rigid object using parameterized ow models and a compact-structure constraint is proposed. While polynomial parameterized ow models have been shown to be eeective in tracking the rigid motion of planar objects, these models are inappropriate for tracking moving objects that change appearance revealing their 3D structure. We extend these models by adding ...

We present a data-driven, project-based algorithm which enhances image resolution by extrapolating high-band wavelet coefficients. High-resolution images are reconstructed by alternating the projections onto two constraint sets: the observation constraint defined by the given low-resolution image and the prior constraint derived from the training data at the high resolution (HR). Two types of p...

Adaptive consistency is a solving algorithm for constraint networks. Its basic step is variable elimination: it takes a network as input, and produces an equivalent network with one less variable and one new constraint (the join of the variable bucket). This process is iterated until every variable is eliminated, and then all solutions can be computed without backtracking. A direct, naive imple...

We propose the use of Soft Constraints as a natural way to model Service Oriented Architecture. In the framework, constraints are used to model components and connectors and constraint aggregation is used to represent their interactions. Moreover, a specific constraint projection operator is used to highlight the service interface. The quality of a service is measured and considered when perfor...

We introduce a general framework for constraint solving where classical CSPs, fuzzy CSPs, weighted CSPs, partial constraint satisfaction, and others can be easily cast. The framework is based on a semiring structure, where the set of the semiring specifies the values to be associated to each tuple of values of the variable domain, and the two semiring operations (+ and x) model constraint proje...