Hierarchical Linear Discriminant Analysis Research Articles

Meta-Information Fusion of Hierarchical Semantics Dependency and Graph Structure for Structured Text Classification

Structured text with plentiful hierarchical structure information is an important part in real-world complex texts. Structured text classification is attracting more attention in natural language processing due to the increasing complexity of application scenarios. Most existing methods treat structured text from a local hierarchy perspective, focusing on the semantics dependency and the graph structure of the structured text independently. However, structured text has global hierarchical structures with sophisticated dependency when compared to unstructured text. According to the variety of structured texts, it is not appropriate to use the existing methods directly. The function of distinction information within semantics dependency and graph structure for structured text, referred to as meta-information, should be stated more precisely. In this article, we propose HGMETA, a novel meta-information embedding frame network for structured text classification, to obtain the fusion embedding of hierarchical semantics dependency and graph structure in a structured text, and to distill the meta-information from fusion characteristics. To integrate the global hierarchical features with fused structured text information, we design a hierarchical LDA module and a structured text embedding module. Specially, we employ a multi-hop message passing mechanism to explicitly incorporate complex dependency into a meta-graph. The meta-information is constructed from meta-graph via neighborhood-based propagation to distill redundant information. Furthermore, using an attention-based network, we investigate the complementarity of semantics dependency and graph structure based on global hierarchical characteristics and meta-information. Finally, the fusion embedding and the meta-information can be straightforwardly incorporated for structured text classification. Experiments conducted on three real-world datasets show the effectiveness of meta-information and demonstrate the superiority of our method.

The discrimination of adopters and non-adopters of conservation agricultural initiatives in northwest Iran: Attitudinal, soil testing, and topographical modules

Over the past decades, agro-ecological measures, such as conservation agricultural initiatives (CAIs) have been widely implemented in developing countries, in like manner with Tabriz city as would be a leading case situated in northwest Iran. In light of linear discriminant analysis (LDA), this cross-sectional research calls on a knowledge gap respecting the differentiation of farmers who become involved in CAIs (adopters) from those not involved (non-adopters), as three data modules, including attitudinal, soil testing, and topographical data, were used. The study takes advantage of a sample of 382 respondents, stratified as regards the geographical location of their farms. Using theory-triangulation, the theoretical foundation enfolds demographic theory, the theory of planned behavior, innovation diffusion model, and resource-based model. The results of hierarchical LDA provide evidence that the hypothesized model is entitled to be fitted with data and accordingly three variables including (1) observability, (2) farm revenue, and (3) compatibility ascertain adopter farmers from non-adopter ones. Furthermore, it was revealed that the soil dynamic quality features, such as electrical conductivity, EC, (dS m−1) (Mann-Whitney U = 353, P < 0.05) and organic carbon content, OC (%), (Mann-Whitney U = 935.5, P < 0.05) were significantly different between the two groups of farms, while carbonate calcium equivalent, CCE (%) (Mann-Whitney U = 935.5, P > 0.05) showed no significant differences. Likewise, the topographical data analyzed in ArcGIS software make manifest that CAIs farms have more steady topographic features rather non-CAI farms and are also situated in the regions with a gradient of less than 5%. In conclusion, the paper delivers management implications for the agricultural extension and authorities of the agricultural research centers who tend to promote CAIs in on-site and off-site farmlands.

Semiautomated identification of a large number of hoverfly (Diptera: Syrphidae) species based on wing measurements

Automated species identification is a promising new method of solving the problem of identification of insects. However, in most previous studies based on this method, the number of identified species was relatively low. In this study, we tested the possibility of the identification of more than 200 species of Syrphidae. The identification was based on wing measurements involving the indication of 13 landmarks. Either single-level linear discriminant analysis (LDA) or hierarchical LDA was used for classification. Identification success varied widely between species. In the case of a single level LDA, 40% of species were identified without error. Overall species classification success of a single level LDA was 89%. In the case of hierarchical LDA, overall classification success was lower; however, identification success at the lowest level of hierarchical classification was higher. Identification based on wing measurements proved to be helpful for the identification of a large number of hoverfly species. Even if the identification of some species was not effective, it allowed the identification of genera or groups of species.

Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject

Objective. We used native sensorimotor representations of fingers in a brain–machine interface (BMI) to achieve immediate online control of individual prosthetic fingers. Approach. Using high gamma responses recorded with a high-density electrocorticography (ECoG) array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: (1) if any finger was moving, and, if so, (2) which digit was moving. To account for sensory feedback, we also mapped the spatiotemporal activation elicited by vibrotactile stimulation. Finally, we used this prediction framework to provide immediate online control over individual fingers of the Johns Hopkins University Applied Physics Laboratory modular prosthetic limb. Main results. The balanced classification accuracy for detection of movements during the online control session was 92% (chance: 50%). At the onset of movement, finger classification was 76% (chance: 20%), and 88% (chance: 25%) if the pinky and ring finger movements were coupled. Balanced accuracy of fully flexing the cued finger was 64%, and 77% had we combined pinky and ring commands. Offline decoding yielded a peak finger decoding accuracy of 96.5% (chance: 20%) when using an optimized selection of electrodes. Offline analysis demonstrated significant finger-specific activations throughout sensorimotor cortex. Activations either prior to movement onset or during sensory feedback led to discriminable finger control. Significance. Our results demonstrate the ability of ECoG-based BMIs to leverage the native functional anatomy of sensorimotor cortical populations to immediately control individual finger movements in real time.

A novel approach for detecting HMDSO poisoning of metal oxide gas sensors and improving their stability by temperature cycled operation

Abstract. In this paper we study the effect of hexamethyldisiloxane (HMDSO) vapor on an SnO2-based gas sensor (GGS 1330, UST Umweltsensortechnik GmbH, Geschwenda, Germany) in a temperature cycled operation (TCO). We show that HMDSO poisoning can be quantified at early stages (85 to 340 ppm × min) with a resolution of ±85 ppm × min using TCO. This novel approach for sensor self-monitoring provides a simple method for early detection of HMDSO poisoning. It is thereby possible to detect poisoning before the sensor function is strongly impaired. In this paper we show that by using an appropriate normalization of the sensor data, the stability of gas discrimination by linear discriminant analysis (LDA) can be improved, which in turn facilitates a more accurate determination of the poisoning state by a hierarchical LDA discrimination. For a specific temperature cycle and feature extraction approach, we show that identification of ethanol and carbon monoxide is still possible after poisoning with 900 ppm × min HMDSO, i.e. a HMDSO poisoning dose more than twice as high as required by DIN EN 50194-1.

Sub-surface probe module equipped with the Karlsruhe Micronose KAMINA using a hierarchical LDA for the recognition of volatile soil pollutants

For the integration in an electric hammer driven soil air probe a battery-operated cylindrical EN module was developed on the basis of the Karlsruhe Micronose KAMINA for rapid depth-resolved soil screening. The EN module contains the complete battery-operated electronics and a 3.5 mm × 3 mm KAMINA gradient microarray chip with a single Pt-doped SnO 2-film. In order to achieve high mechanical stability and low power consumption the microarray chip is fixed only with bond wires within the clearance of a ceramic carrier card. Test exposures of the module were carried out with a variety of potential soil pollutants (such as chlorinated, aromatic, aliphatic hydrocarbons in air with 80% r.h.) in order to investigate the KAMINA's gas analytical performance. The detection limits were <1 mg/m 3 for aromatic and aliphatic hydrocarbons, except 10–500 mg/m 3 for chlorinated hydrocarbons at response times <100 s. The gas discrimination power was found appropriate to distinguish between these type of pollutants. A novel hierarchical linear discriminant analysis has been developed for online classification of volatile soil pollution.

Depth Perception from a 2D Natural Scene Using Scale Variation of Texture Patterns

In this paper, we introduce a new method for depth perception from a 2D natural scene using scale variation of patterns. As the surface from a 2D scene gets farther away from us, the texture appears finer and smoother. Texture gradient is one of the monocular depth cues which can be represented by gradual scale variations of textured patterns. To extract feature vectors from textured patterns, higher order local autocorrelation functions are utilized at each scale step. The hierarchical linear discriminant analysis is employed to classify the scale rate of the feature vector which can be divided into subspaces by recursively grouping the overlapped classes. In the experiment, relative depth perception of 2D natural scenes is performed on the proposed method and it is expected to play an important role in natural scene analysis.