“Small Sample Size”: a methodological problem in bayes plug-in classifier for image recognition

New technologies in the form of improved instrumentation have made it possible to take detailed measurements over recognition patterns. This increase in the number of features or parameters for each pattern of interest not necessarily generates better classification performance. In fact, in problems where the number of training samples is less than the number of parameters, i.e. “small sample size” problems, not all parameters can be estimated and traditional classifiers often used to analyse lower dimensional data deteriorate. The Bayes plug-in classifier has been successfully applied to discriminate high dimensional data. This classifier is based on similarity measures that involve the inverse of the sample group covariance matrices. However, these matrices are singular in “small sample size” problems. Thus, several other methods of covariance estimation have been proposed where the sample group covariance estimate is replaced by covariance matrices of various forms. In this report, some of these approaches are reviewed and a new covariance estimator is proposed. The new estimator does not require an optimisation procedure, but an eigenvectoreigenvalue ordering process to select information from the projected sample group covariance matrices whenever possible and the pooled covariance otherwise. The effectiveness of the method is shown by some experimental results

A maximum uncertainty LDA-based approach for limited sample size problems – with application to Face Recognition

A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instability of the within-class scatter matrix. In practice, particularly in image recognition applications such as face recognition, there are often a large number of pixels or pre-processed features available, but the total number of training patterns is limited and commonly less than the dimension of the feature space. In this paper, a new LDA-based method is proposed. It is based on a straighforward stabilisation approach for the within-class scatter matrix. In order to evaluate its effectiveness, experiments on face recognition using the well-known ORL and FERET face databases were carried out and compared with other LDA-based methods. The results indicate that our method improves the LDA classification performance when the within-class scatter matrix is not only singular but also poorly estimated, with or without a Principal Component Analysis intermediate step and using less linear discriminant features

Using an holistic method based on prior information to represent global and local variations on face images

Faces are familiar objects that can be easily perceived and recognized by ourselves. However, the computational modeling of such apparently natural human ability remains challenging. Recent studies in the literature have suggested that face processing is a cognition task composed of configural (or global) and featural (or local) sources of information, but with controversial arguments about the combination of these two types of information. In this work, we describe an holistic method that combines variance used in Principal Component Analysis (PCA) with some prior knowledge about the underlying visual perception task, including systematically the global and local information in the common multivariate representation of face images. We have showed that, with prior information, important local variations represented by principal components with small eigenvalues may not be discarded augmenting the classification accuracy of the first orthogonal basis vectors. Most interestingly, PCA with prior knowledge provides a specialized feature selection procedure, where the mapping of high-dimensional data into a lower-dimensional space has been able to handle local variations and capture not only the entire facial appearance but also some sample group facial features

An enigmatic hypoplastic defect of the maxillary lateral incisor in recent and fossil orangutans from Sumatra (Pongo abelii) and Borneo (Pongo pygmaeus)

Developmental dental pathologies provide insight into health of primates during ontogeny, and are particularly useful for elucidating the environment in which extant and extinct primates matured. Our aim is to evaluate whether the prevalence of an unusual dental defect on the mesiolabial enamel of the upper lateral incisor, thought to reflect dental crowding during maturation, is lesser in female orangutans, with their smaller teeth, than in males; and in Sumatran orangutans, from more optimal developmental habitats, than in those from Borneo. Our sample includes 49 Pongo pygmaeus (87 teeth), 21 P. abelii (38 teeth), Late Pleistocene paleo-orangutans from Sumatra and Vietnam (67 teeth), Late Miocene catarrhines Lufengpithecus lufengensis (2 teeth), and Anapithecus hernyaki (7 teeth). Methods include micro-CT scans, radiography, and dental metrics of anterior teeth. We observed fenestration between incisor crypts and marked crowding of unerupted crowns, which could allow tooth-to-tooth contact. Tooth size does not differ significantly in animals with or without the defect, implicating undergrowth of the jaw as the proximate cause of dental crowding and defect presence. Male orangutans from both islands show more defects than do females. The defect is significantly more common in Bornean orangutans (71 %) compared to Sumatran (29 %). Prevalence among fossil forms falls between these extremes, except that all five individual Anapithecus show one or both incisors with the defect. We conclude that maxillary lateral incisor defect is a common developmental pathology of apes that is minimized in optimal habitats and that such evidence can be used to infer habitat quality in extant and fossil apes

The First Bromeligenous Species of Dendropsophus (Anura: Hylidae) from Brazil\u27s Atlantic Forest

We describe a new treefrog species of Dendropsophus collected on rocky outcrops in the Brazilian Atlantic Forest. Ecologically, the new species can be distinguished from all known congeners by having a larval phase associated with rainwater accumulated in bromeliad phytotelms instead of temporary or lentic water bodies. Phylogenetic analysis based on molecular data confirms that the new species is a member of Dendropsophus; our analysis does not assign it to any recognized species group in the genus. Morphologically, based on comparison with the 96 known congeners, the new species is diagnosed by its small size, framed dorsal color pattern, and short webbing between toes IV-V. The advertisement call is composed of a moderate-pitched two-note call (~5 kHz). The territorial call contains more notes and pulses than the advertisement call. Field observations suggest that this new bromeligenous species uses a variety of bromeliad species to breed in, and may be both territorial and exhibit male parental care

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Hydrology Affects Environmental and Spatial Structuring of Microalgal Metacommunities in Tropical Pacific Coast Wetlands

The alternating climate between wet and dry periods has important effects on the hydrology and therefore on niche-based processes of water bodies in tropical areas. Additionally, assemblages of microorganism can show spatial patterns, in the form of a distance decay relationship due to their size or life form. We aimed to test spatial and environmental effects, modulated by a seasonal flooding climatic pattern, on the distribution of microalgae in 30 wetlands of a tropical dry forest region: the Pacific coast of Costa Rica and Nicaragua. Three surveys were conducted corresponding to the beginning, the highest peak, and the end of the hydrological year during the wet season, and species abundance and composition of planktonic and benthic microalgae was determined. Variation partitioning analysis (as explained by spatial distance or environmental factors) was applied to each seasonal dataset by means of partial redundancy analysis. Our results show that microalgal assemblages were structured by spatial and environmental factors depending on the hydrological period of the year. At the onset of hydroperiod and during flooding, neutral effects dominated community dynamics, but niche-based local effects resulted in more structured algal communities at the final periods of desiccating water bodies. Results suggest that climatemediated effects on hydrology can influence the relative role of spatial and environmental factors on metacommunities of microalgae. Such variability needs to be accounted in order to describe accurately community dynamics in tropical coastal wetlands.Agencia Española de Cooperación y Desarrollo/[A1024073/09]/AECID/EspañaAgencia Española de Cooperación y Desarrollo/[A/031019/10]/AECID/EspañaAgencia Española de Cooperación y Desarrollo/[C/032994/10]/AECID/EspañaAgencia Española de Cooperación y Desarrollo/[A3/ 036594/11]/AECID/EspañaUniversidad de Costa Rica/[741-B1-517]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Instituto Clodomiro Picado (ICP)UCR::Vicerrectoría de Docencia::Salud::Facultad de Microbiologí