Abstract. Embedding high-dimensional patterns in low-dimensional latent spaces is a challenging task. In this paper, we introduce re-sampling strategies to leave local optima in the data space reconstruction error (DSRE) minimization process of unsupervised kernel regression (UKR). For this sake, we concentrate on a hybrid UKR variant that combines iterative solution construction with gradient ...

Wepropose a novel, simple, efficient and distribution-free re-sampling technique for developing prediction intervals for returns and volatilities following ARCH/GARCH models. In particular, our key idea is to employ a Box-Jenkins linear representation of an ARCH/GARCH equation and then to adapt a sieve bootstrap procedure to the non-linear GARCH framework. Our simulation studies indicate that t...

Cloth-changing person re-identification (re-ID) is a new rising research topic that aims at retrieving pedestrians whose clothes are changed. This task quite challenging and has not been fully studied to date. Current works mainly focus on body shape or contour sketch, but they robust enough due view posture variations. The key this exploit cloth-irrelevant cues. paper proposes semantic-guided ...

Real world data sets often contain disproportionate sample sizes of observed groups making the task of prediction algorithms very difficult. One of the many ways to combat inherit bias from class imbalance data is to perform re-sampling. In this paper we discuss two popular re-sampling approaches proposed in literature, Synthetic Minority Over-sampling Technique (SMOTE) and Propensity Score Mat...