OBJECTIVE To study the psychometric characteristics of German version of the Hospital Survey on Patient Safety Culture and to compare its dimensionality to other language versions in order to understand the instrument's potential for cross-national studies. DESIGN Cross-sectional multicentre study to establish psychometric properties of German version of the survey instrument. SETTING 73 un...

The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN  of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most      relevant information of X. These eigenvectors are called principal components [8]. Ass...

The performance of nearest neighbor (NN) queries degrades noticeably with increasing di-mensionality of the data. This stems not only from reduced selectivity of high-dimensional data but also from an increased number of seek operations during query execution. We propose a new framework to transform NN queries into at most two range queries. This is achieved by rst estimating the NN-radius, per...

The metric space model abstracts many proximity search problems, from nearest-neighbor classifiers to textual and multimedia information retrieval. In this context, an index is a data structure that speeds up proximity queries. However, indexes lose their efficiency as the intrinsic data dimensionality increases. In this paper we present a simple index called list of clusters (LC), which is bas...

In this paper, a new language identification system is presented based on the total variability approach previously developed in the field of speaker identification. Various techniques are employed to extract the most salient features in the lower dimensional i-vector space and the system developed results in excellent performance on the 2009 LRE evaluation set without the need for any post-pro...

Recently DTW (dynamic time warping) has been recognized as the most robust distance function to measure the similarity between two time series, and this fact has spawned a flurry of research on this topic. Most indexing methods proposed for DTW are based on the R-tree structure. Because of high dimensionality and loose lower bounds for time warping distance, the pruning power of these tree stru...

Local smoothing testing based on multivariate nonparametric regression estimation is one of the main model checking methodologies in the literature. However, the relevant tests suffer from typical curse of dimensionality, resulting in slow convergence rates to their limits under the null hypothesis and less deviation from the null hypothesis under alternative hypotheses. This problem prevents t...

Let Z2 = {0, 1} and G = (V ,E) be a graph. A labeling f : V → Z2 induces an edge labeling f* : E →Z2 defined by f*(uv) = f(u).f (v). For i ε Z2 let vf (i) = v(i) = card{v ε V : f(v) = i} and ef (i) = e(i) = {e ε E : f*(e) = i}. A labeling f is said to be Vertex-friendly if | v(0) − v(1) |≤ 1. The vertex balance index set is defined by {| ef (0) − ef (1) | : f is vertex-friendly}. In this paper ...

A new solution to the additive constant problem in metric multidimensional scaling is developed. This solution determines, for a given dimensionality, the additive constant and the resulting stimulus projections on the dimensions of a Euclidean space which minimize the sum of squares of discrepancies between the formal model for metric multidimensional scaling and the original data. A modificat...