Feature selection in a low cost signature recognition system based on normalized signatures and fractional distances

Producción CientíficaIn a previous work a new proposal for an efficient on-line signature recognition system with very low computational load and storage requirements was presented. This proposal is based on the use of size normalized signatures, which allows for similarity estimation, usually based on DTW or HMMs, to be performed by an easy distance calcultaion between vectors, which is computed using fractional distance. Here, a method to select representative features from the normalized signatures is presented. Only the most stable features in the training set are used for distance estimation. This supposes a larger reduction in system requirements, while the system performance is increased. The verification task has been carried out. The results achieved are about 30% and 20% better with skilled and random forgeries, respectively, than those achieved with a DTW-based system, with storage requirements between 15 and 142 times lesser and a processing speed between 274 and 926 times greater. The security of the system is also enhanced as only the representative features need to be stored, it being impossible to recover the original signature from these.Junta de Castilla y Leon (project VA077A08

Multiple Instance Hybrid Estimator for Learning Target Signatures

Signature-based detectors for hyperspectral target detection rely on knowing the specific target signature in advance. However, target signature are often difficult or impossible to obtain. Furthermore, common methods for obtaining target signatures, such as from laboratory measurements or manual selection from an image scene, usually do not capture the discriminative features of target class. In this paper, an approach for estimating a discriminative target signature from imprecise labels is presented. The proposed approach maximizes the response of the hybrid sub-pixel detector within a multiple instance learning framework and estimates a set of discriminative target signatures. After learning target signatures, any signature based detector can then be applied on test data. Both simulated and real hyperspectral target detection experiments are shown to illustrate the effectiveness of the method

Classical Signature Change in the Black Hole Topology

Investigations of classical signature change have generally envisaged applications to cosmological models, usually a Friedmann-Lemaitre-Robertson-Walker model. The purpose has been to avoid the inevitable singularity of models with purely Lorentzian signature, replacing the neighbourhood of the big bang with an initial, singularity free region of Euclidean signture, and a signature change. We here show that signature change can also avoid the singularity of gravitational collapse. We investigate the process of re-birth of Schwarzschild type black holes, modelling it as a double signature change, joining two universes of Lorentzian signature through a Euclidean region which provides a `bounce'. We show that this process is viable both with and without matter present, but realistic models -- which have the signature change surfaces hidden inside the horizons -- require non-zero density. In fact the most realistic models are those that start as a finite cloud of collapsing matter, surrounded by vacuum. We consider how geodesics may be matched across a signature change surface, and conclude that the particle `masses' must jump in value. This scenario may be relevant to Smolin's recent proposal that a form of natural selection operates on the level of universes, which favours the type of universe we live in.Comment: LaTeX, 19 pages, 11 Figures. Replacement - only change is following comment: For a pdf version with the figures embedded, see http://www.mth.uct.ac.za/~cwh/mypub.htm

Separable mechanisms underlying global feature-based attention

Feature-based attention is known to operate in a spatially global manner, in that the selection of attended features is not bound to the spatial focus of attention. Here we used electromagnetic recordings in human observers to characterize the spatiotemporal signature of such global selection of an orientation feature. Observers performed a simple orientation-discrimination task while ignoring task-irrelevant orientation probes outside the focus of attention. We observed that global feature-based selection, indexed by the brain response to unattended orientation probes, is composed of separable functional components. One such component reflects global selection based on the similarity of the probe with task-relevant orientation values ("template matching"), which is followed by a component reflecting selection based on the similarity of the probe with the orientation value under discrimination in the focus of attention ("discrimination matching"). Importantly, template matching occurs at similar to 150 ms after stimulus onset, similar to 80 ms before the onset of discrimination matching. Moreover, source activity underlying template matching and discrimination matching was found to originate from ventral extrastriate cortex, with the former being generated in more anterolateral and the latter in more posteromedial parts, suggesting template matching to occur in visual cortex higher up in the visual processing hierarchy than discrimination matching. We take these observations to indicate that the population-level signature of global feature-based selection reflects a sequence of hierarchically ordered operations in extrastriate visual cortex, in which the selection based on task relevance has temporal priority over the selection based on the sensory similarity between input representations

Exploring signature multiplicity in microarray data using ensembles of randomized trees

A challenging and novel direction for feature selection research in computational biology is the analysis of signature multiplicity. In this work, we propose to investigate the eect of signature multiplicity on feature importance scores derived from tree-based ensemble methods. We show that looking at individual tree rankings in an ensemble could highlight the existence of multiple signatures and we propose a simple post-processing method based on clustering that can return smaller signatures with better predictive performance than signatures derived from the global tree ranking at almost no additional cost