The evolving tree-analysis and applications

In this paper, we enhance and analyze the Evolving Tree (ETree) data analysis algorithm. The suggested improvements aim to make the system perform better while still maintaining the simple nature of the basic algorithm. We also examine the system's behavior with many different kinds of tests, measurements and visualizations. We compare the ETree's performance against classical data analysis methods and very similar modern systems. We find that the ETree is a suitable method for unsupervised analysis of huge data sets.     

Nonlinear dynamical factor analysis for state change detection

Changes in a dynamical process are often detected by monitoring selected indicators directly obtained from the process observations, such as the mean values or variances. Standard change detection algorithms such as the Shewhart control charts or the cumulative sum (CUSUM) algorithm are often based on such first- and second-order statistics. Much better results can be obtained if the dynamical process is properly modeled, for example by a nonlinear state-space model, and then the accuracy of the model is monitored over time. The success of the latter approach depends largely on the quality of the model. In practical applications like industrial processes, the state variables, dynamics, and observation mapping are rarely known accurately. Learning from data must be used; however, methods for the simultaneous estimation of the state and the unknown nonlinear mappings are very limited. We use a novel method of learning a nonlinear state-space model, the nonlinear dynamical factor analysis (NDFA) algorithm. It takes a set of multivariate observations over time and fits blindly a generative dynamical latent variable model, resembling nonlinear independent component analysis. We compare the performance of the model in process change detection to various traditional methods. It is shown that NDFA outperforms the classical methods by a wide margin in a variety of cases where the underlying process dynamics changes.     

Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery

The k-Nearest Neighbor (k-NN) technique has become extremely popular for a variety of forest inventory mapping and estimation applications. Much of this popularity may be attributed to the non-parametric, multivariate features of the technique, its intuitiveness, and its ease of use. When used with satellite imagery and forest inventory plot data, the technique has been shown to produce useful estimates of many forest attributes including forest/non-forest, volume, and basal area. However, variance estimators for quantifying the uncertainty of means or sums of k-NN pixel-level predictions for areas of interest (AOI) consisting of multiple pixels have not been reported. The primary objectives of the study were to derive variance estimators for AOI estimates obtained from k-NN predictions and to compare precision estimates resulting from different approaches to k-NN prediction and different interpretations of those predictions. The approaches were illustrated by estimating proportion forest area, tree volume per unit area, tree basal area per unit area, and tree density per unit area for 10-km AOIs. Estimates obtained using k-NN approaches and traditional inventory approaches were compared and found to be similar. Further, variance estimates based on different interpretations of k-NN predictions were similar. The results facilitate small area estimation and simultaneous and consistent mapping and estimation of multiple forest attributes.     

Variation among and within mountain birch trees in foliage phenols,carbohydrates, and amino acids,and in growth ofEpirrita autumnata larvae

Leaf quality of the mountain birch (Betula pubescens ssp.tortuosa) for herbivores was studied at several hierarchical levels: among trees, among ramets within trees, among branches within ramets, and among short shoots within branches. The experimental units at each level were chosen randomly. The indices of leaf quality were the growth rate of the larvae of a geometrid,Epirrita autumnata, and certain biochemical traits of the leaves (total phenolics and individual phenolic compounds, total carbohydrates and individual sugars, free and protein-bound amino acids). We also discuss relationships between larval growth rate and biochemical foliage traits. Larval growth rates during two successive years correlated positively at the level of tree, the ramet, and the branch, indicating that the relationships in leaf quality remained constant between seasons both among and within trees. The distribution of variation at different hierarchical levels depended on the trait in question. In the case of larval growth rate, ramets and short shoots accounted for most of the explained variation. In the case of biochemical compounds, trees accounted for most of the variance in the content of total phenolics and individual low-molecular-weight phenolics. In the content of carbohydrates (total carbohydrates, starch, fructose, glucose, and sucrose) and amino acids, variation among branches was generally larger than variation among trees. Variation among ramets was low for most compounds. No single leaf trait played a paramount role in larval growth. Secondary compounds, represented by phenolic compounds, or primary metabolites, particularly sugars, may both be important in determining the suitability of birch leaves for larvae. If phenols are causally more important, genet-specific analyses of foliage chemistry are needed. If sugars are of primary importance, within-genet sampling and analysis of foliage chemistry are necessary.     

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Conservation and land use planning in humid tropical lowland forests urgently need accurate remote sensing techniques to distinguish among floristically different forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain forest types can be accurately discriminated by a non-parametric k nearest neighbors (k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of forest (regrowth forest). Three floristically defined forest types were formed through clustering the old-growth forest plots (n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four forest types did not adequately distinguish two old-growth forest classes, so they were merged into a single forest class. The resulting three forest classes were most accurately classified by the k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the k-nn classifier can accurately distinguish floristically and structurally different rain forest types. The classification accuracies were higher for the k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.     

Results of Finnish national survey on EU legislation concerning computer work

The European Directive on computer work (VDU 90/270/EEC) is implemented in the Finnish Government Decree. The aim of the present study was to evaluate the effects of the legislation and its applications in practice. The quantitative method used an online questionnaire. The respondents were employers (N = 934), employees (N = 1872) and occupational health care (OHC) units (N = 289). The majority of all these three groups considered the provisions clear and easy to understand, comprehensive, and easy to comply with. The provisions had a great impact on preventing mental overloading, arranging sight tests at the employer's cost, and on the refunding of eyeglasses for computer work. Most employers felt they did not need any more specific provisions, whereas half of the employees and OHC professionals would have liked the provisions to be more detailed. More explicit and specific regulations were also needed for practice applications in particular, as well as for the refunding of the costs of special eyeglasses for computer work. A total of 59% of the employers reported that costs of glasses are refunded, whereas the figure for employees was clearly lower (37%). OHC professionals reported that the costs of glasses were refunded in 82% of their customer workplaces. The practical conclusion is that employees' sight examinations and compensation for eyeglasses should be promoted in computer work. Moreover, ergonomic applications are best carried out in co-operation with OHC personnel.     

Classification Based on Hybridization of Parametric and Nonparametric Classifiers

Parametric methods of classification assume specific parametric models for competing population densities (e.g., Gaussian population densities can lead to linear and quadratic discriminant analysis) and they work well when these model assumptions are valid. Violation in one or more of these parametric model assumptions often leads to a poor classifier. On the other hand, nonparametric classifiers (e.g., nearest-neighbor and kernel-based classifiers) are more flexible and free from parametric model assumptions. But, the statistical instability of these classifiers may lead to poor performance when we have small numbers of training sample observations. Nonparametric methods, however, do not use any parametric structure of population densities. Therefore, even when one has some additional information about population densities, that important information is not used to modify the nonparametric classification rule. This paper makes an attempt to overcome these limitations of parametric and nonparametric approaches and combines their strengths to develop some hybrid classification methods. We use some simulated examples and benchmark data sets to examine the performance of these hybrid discriminant analysis tools. Asymptotic results on their misclassification rates have been derived under appropriate regularity conditions.     

An efficient deconvolution algorithm for estimating oxygen consumption during muscle activities

The reconstruction of an unknown input function from noisy measurements in a biological system is an ill-posed inverse problem. Any computational algorithm for its solution must use some kind of regularization technique to neutralize the disastrous effects of amplified noise components on the computed solution. In this paper, following a hierarchical Bayesian statistical inversion approach, we seek estimates for the input function and regularization parameter (hyperparameter) that maximize the posterior probability density function. We solve the maximization problem simultaneously for all unknowns, hyperparameter included, by a suitably chosen quasi-Newton method. The optimization approach is compared to the sampling-based Bayesian approach. We demonstrate the efficiency and robustness of the deconvolution algorithm by applying it to reconstructing the time courses of mitochondrial oxygen consumption during muscle state transitions (e.g., from resting state to contraction and recovery), from the simulated noisy output of oxygen concentration dynamics on the muscle surface. The model of oxygen transport and metabolism in skeletal muscle assumes an in vitro cylindrical structure of the muscle in which the oxygen from the surrounding oxygenated solution diffuses into the muscle and is then consumed by the muscle mitochondria. The algorithm can be applied to other deconvolution problems by suitably replacing the forward model of the system.     

Quadratic nonnegative matrix factorization

In Nonnegative Matrix Factorization (NMF), a nonnegative matrix is approximated by a product of lower-rank factorizing matrices. Most NMF methods assume that each factorizing matrix appears only once in the approximation, thus the approximation is linear in the factorizing matrices. We present a new class of approximative NMF methods, called Quadratic Nonnegative Matrix Factorization (QNMF), where some factorizing matrices occur twice in the approximation. We demonstrate QNMF solutions to four potential pattern recognition problems in graph partitioning, two-way clustering, estimating hidden Markov chains, and graph matching. We derive multiplicative algorithms that monotonically decrease the approximation error under a variety of measures. We also present extensions in which one of the factorizing matrices is constrained to be orthogonal or stochastic. Empirical studies show that for certain application scenarios, QNMF is more advantageous than other existing nonnegative matrix factorization methods.