The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the discrepancy between domain-specific latent feature representations directly in the hidden activation space. Although some standard distribution matching approaches exist that can be interpreted as the matching of weigh...

The 2D face recognition systems encounter difficulties in recognizing faces with illumination variations. The depth map of the 3D face data has the potential to handle the variation in illumination of face images. The view variations are handled by using the moment invariants. Moment Invariants are used as rotation invariant features of the face image. For feature matching an efficient fuzzy-ne...

Abstract. A BDDC (balancing domain decomposition by constraints) algorithm is developed for elasticity problems in three dimensions with mortar discretization on geometrically nonconforming subdomain partitions. Coarse basis functions in the BDDC algorithm are constructed from primal constraints on faces. These constrains are similar to the average matching condition and the moment matching con...

In this paper we propose a methodology for estimating preference parameters in matching models. Our estimator applies to repeated observations of matchings among a fixed group of individuals, which is a similar data structure as in Fox (2010). Our estimator is based on stability conditions in the matching models; we consider both transferable (TU) and non-transferable utility (NTU) models. In b...

The package fitdistrplus provides functions for fitting univariate distributions to different types of data (continuous censored or non-censored data and discrete data) and allowing different estimation methods (maximum likelihood, moment matching, quantile matching and maximum goodness-of-fit estimation). Outputs of fitdist and fitdistcens functions are S3 objects, for which kind generic metho...

Bootstrap particle filter as a subcase of APF Here we will show that when q is a delta function, APF recovers the bootstrap particle filter. The Dirac delta function can be considered as the limit of a Gaussian as the variance goes to zero, δ(θ − μ) = limσ2→0N (θ;μ, σ). Therefore, we can view q as an exponential family distribution. Specifically we are dealing with a Gaussian distribution with ...

Density ratio estimation has attracted a great deal of attention in the statistics and machine learning communities since it can be used for solving various statistical data processing tasks such as non-stationarity adaptation, two-sample test, outlier detection, independence test, feature selection/extraction, independent component analysis, causal inference, and conditional probability estima...

This paper proposes a robust and effective shape feature, which is based on a set of orthogonal complex moments of images known as Zernike moments. Zernike moment phase is usually not used in image description since it’s sensitive to image rotations. However, phase captures important image information, which is revealed by our numerical analysis of image reconstruction. We therefore propose com...

For the whole matching cannot handle partial occlusion and lack of specificity, a new method using PolarRadius-Invariant-Moment, which is based on Key-Points to extract features for target’s shape recognition, is presented in this paper. Firstly, key-points of the hand shape are extracted through discrete curve evolution method. Secondly, Polar-Radius-Invariant-Moment based on KeyPoints is used...

The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the domain-specific latent feature representations directly in the hidden activation space. Although some standard distribution matching approaches exist that can be interpreted as the matching of weighted sums of moments,...