Markus Breitenbach

Women’s Pathways to Serious and Habitual Crime A Person-Centered Analysis Incorporating Gender Responsive Factors

Qualitative approaches for identifying and characterizing women’s pathways to crime are being augmented by quantitative methods. This study applies quantitative taxonomic methods in disaggregating a large sample of women offenders from a prison population to identify diverse pathway prototypes. An array of gender-responsive and gender-neutral factors and full criminal histories was used to characterize each pathway. Cross-sample and cross-method replication tests demonstrated the stable replication of these pathways. The identified prototypes were related to the prior literature, including Daly’s pathway models, Moffitt’s developmental taxonomy, and several prior taxonomic studies of women’s pathways. Eight reliable pathways were identified that were nested within four broad, superordinate pathway categories. Substantial links to the prior pathways literature were noted, although greater complexity was found to exist in the eight identified pathways.

Clustering through ranking on manifolds

Clustering aims to find useful hidden structures in data. In this paper we present a new clustering algorithm that builds upon the consistency method (Zhou, et.al., 2003), a semi-supervised learning technique with the property of learning very smooth functions with respect to the intrinsic structure revealed by the data. Other methods, e.g. Spectral Clustering, obtain good results on data that reveals such a structure. However, unlike Spectral Clustering, our algorithm effectively detects both global and within-class outliers, and the most representative examples in each class. Furthermore, we specify an optimization framework that estimates all learning parameters, including the number of clusters, directly from data. Finally, we show that the learned cluster-models can be used to add previously unseen points to clusters without re-learning the original cluster model. Encouraging experimental results are obtained on a number of real world problems.

The Directional Attack on Wireless Localization -or- How to Spoof Your Location with a Tin Can

802.11 localization algorithms provide the ability to accurately position and track wireless clients thereby enabling location-based services and applications. However, we show that these localization techniques are vulnerable to noncryptographic attacks where an adversary uses a low-cost directional antenna to appear from the localization algorithms perspective to be in another arbitrary location of their choosing. The attackers ability to actively influence where they are positioned is a key distinguishing feature of the directional attack relative to prior localization attacks that use transmit power control to introduce localization errors. We implement a representative set of received signal strength-based localization algorithms and evaluate the attack in a real office building environment. To mitigate the attacks effectiveness, we develop and evaluate an attack detection scheme that offers a high detection rate with few false positives.

Nonparametric classification with polynomial MPMC cascades

A new class of nonparametric algorithms for high-dimensional binary classification is proposed using cascades of low dimensional polynomial structures. Construction of polynomial cascades is based on Minimax Probability Machine Classification (MPMC), which results in direct estimates of classification accuracy, and provides a simple stopping criteria that does not require expensive cross-validation measures. This Polynomial MPMC Cascade (PMC) algorithm is constructed in linear time with respect to the input space dimensionality, and linear time in the number of examples, making it a potentially attractive alternative to algorithms like support vector machines and standard MPMC. Experimental evidence is given showing that, compared to state-of-the-art classifiers, PMCs are competitive; inherently fast to compute; not prone to overfitting; and generally yield accurate estimates of the maximum error rate on unseen data.