Fidelity-Commensurability Tradeoff in Joint Embedding of Disparate Dissimilarities

In various data settings, it is necessary to compare observations from disparate data sources. We assume the data is in the dissimilarity representation (P?kalska and Duin, 2005) and investigate a joint embedding method (Priebe et al., 2013) that results in a commensurate representation of disparate dissimilarities. We further assume that there are “matched” observations from different conditions which can be considered to be highly similar, for the sake of inference. The joint embedding results in the joint optimization of fidelity (preservation of within-condition dissimilarities) and commensurability (preservation of between-condition dissimilarities between matched observations). We show that the tradeoff between these two criteria can be made explicit using weighted raw stress as the objective function for multidimensional scaling. In our investigations, we use a weight parameter, w, to control the tradeoff, and choose match detection as the inference task. Our results show weights that are optimal (with respect to the inference task) are different than equal weights for commensurability and fidelity and the proposed weighted embedding scheme provides significant improvements in statistical power.     

Paternal origin of new mutations in von Recklinghausen neurofibromatosis

Von Recklinghausen neurofibromatosis (NF-1) is a common autosomal dominant disorder. The estimated new mutation rate (1 x 10(-4] is one of the highest for a human disorder. Here we report that in 12 of 14 families we have analysed, the new mutation is of paternal origin. This result is similar to that recently obtained for retinoblastoma. In other genetic disorders that show a bias towards paternal origin of new mutations, there is a marked increase in the incidence of mutations with paternal age, consistent with the mutations arising from replication errors in mitosis of spermatogonial stem cells. In retinoblastoma and NF-1, however, such paternal age effects are slight or absent. The mechanism or timing of germline mutation could therefore be different in the two cases.     

A Hierarchical Methodology for Class Detection Problems with Skewed Priors

We describe a novel extension to the Class-Cover-Catch-Digraph (CCCD) classifier, specifically tuned to detection problems. These are two-class classification problems where the natural priors on the classes are skewed by several orders of magnitude. The emphasis of the proposed techniques is in computationally efficient classification for real-time applications. Our principal contribution consists of two boosted classi- fiers built upon the CCCD structure, one in the form of a sequential decision process and the other in the form of a tree. Both of these classifiers achieve performances comparable to that of the original CCCD classifiers, but at drastically reduced computational expense. An analysis of classification performance and computational cost is performed using data from a face detection application. Comparisons are provided with Support Vector Machines (SVM) and reduced SVMs. These comparisons show that while some SVMs may achieve higher classification performance, their computational burden can be so high as to make them unusable in real-time applications. On the other hand, the proposed classifiers combine high detection performance with extremely fast classification.