scholarly journals Laplacian Regularized Spatial-Aware Collaborative Graph for Discriminant Analysis of Hyperspectral Imagery

2018 ◽  
Vol 11 (1) ◽  
pp. 29 ◽  
Author(s):  
Xinwei Jiang ◽  
Xin Song ◽  
Yongshan Zhang ◽  
Junjun Jiang ◽  
Junbin Gao ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Spatial Information ◽  
Feature Reduction ◽  
Collaborative Representation ◽  
Data Sets ◽  
Analysis Framework ◽  
Spatial Constraint ◽  
Spatial Constraints ◽  
Low Dimensional ◽  
Laplacian Regularization

Dimensionality Reduction (DR) models are of significance to extract low-dimensional features for Hyperspectral Images (HSIs) data analysis where there exist lots of noisy and redundant spectral features. Among many DR techniques, the Graph-Embedding Discriminant Analysis framework has demonstrated its effectiveness for HSI feature reduction. Based on this framework, many representation based models are developed to learn the similarity graphs, but most of these methods ignore the spatial information, resulting in unsatisfactory performance of DR models. In this paper, we firstly propose a novel supervised DR algorithm termed Spatial-aware Collaborative Graph for Discriminant Analysis (SaCGDA) by introducing a simple but efficient spatial constraint into Collaborative Graph-based Discriminate Analysis (CGDA) which is inspired by recently developed Spatial-aware Collaborative Representation (SaCR). In order to make the representation of samples on the data manifold smoother, i.e., similar pixels share similar representations, we further add the spectral Laplacian regularization and propose the Laplacian regularized SaCGDA (LapSaCGDA), where the two spectral and spatial constraints can exploit the intrinsic geometric structures embedded in HSIs efficiently. Experiments on three HSIs data sets verify that the proposed SaCGDA and LapSaCGDA outperform other state-of-the-art methods.

scholarly journals Single molecule tracking and analysis framework including theory-predicted parameter settings

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Timo Kuhn ◽  
Johannes Hettich ◽  
Rubina Davtyan ◽  
J. Christof M. Gebhardt
Keyword(s):  
Single Molecule ◽  
Spatial Information ◽  
Reaction Rates ◽  
Data Sets ◽  
Analysis Framework ◽  
Tracking Errors ◽  
Experimental Conditions ◽  
Single Molecule Tracking ◽  
External Software ◽  
User Friendly

AbstractImaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require additional manual steps to analyze whole data sets or to compare different experimental conditions. We report a fast and comprehensive single molecule tracking and analysis framework (TrackIt) to simultaneously process several multi-movie data sets. A user-friendly GUI offers convenient tracking visualization, multiple state-of-the-art analysis procedures, display of results, and data im- and export at different levels to utilize external software tools. We applied our framework to quantify dissociation rates of a transcription factor in the nucleus and found that tracking errors, similar to fluorophore photobleaching, have to be considered for reliable analysis. Accordingly, we developed an algorithm, which accounts for both tracking losses and suggests optimized tracking parameters when evaluating reaction rates. Our versatile and extensible framework facilitates quantitative analysis of single molecule experiments at different experimental conditions.

scholarly journals Multi-Feature Manifold Discriminant Analysis for Hyperspectral Image Classification

2019 ◽  
Vol 11 (6) ◽  
pp. 651 ◽  
Author(s):  
Hong Huang ◽  
Zhengying Li ◽  
Yinsong Pan
Keyword(s):  
Discriminant Analysis ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Local Binary Patterns ◽  
Hyperspectral Data ◽  
Spectral Features ◽  
Spatial Features ◽  
Combined Features ◽  
Low Dimensional

Hyperspectral image (HSI) provides both spatial structure and spectral information for classification, but many traditional methods simply concatenate spatial features and spectral features together that usually lead to the curse-of-dimensionality and unbalanced representation of different features. To address this issue, a new dimensionality reduction (DR) method, termed multi-feature manifold discriminant analysis (MFMDA), was proposed in this paper. At first, MFMDA explores local binary patterns (LBP) operator to extract textural features for encoding the spatial information in HSI. Then, under graph embedding framework, the intrinsic and penalty graphs of LBP and spectral features are constructed to explore the discriminant manifold structure in both spatial and spectral domains, respectively. After that, a new spatial-spectral DR model for multi-feature fusion is built to extract discriminant spatial-spectral combined features, and it not only preserves the similarity relationship between spectral features and LBP features but also possesses strong discriminating ability in the low-dimensional embedding space. Experiments on Indian Pines, Heihe and Pavia University (PaviaU) hyperspectral data sets demonstrate that the proposed MFMDA method performs significantly better than some state-of-the-art methods using only single feature or simply stacking spectral features and spatial features together, and the classification accuracies of it can reach 95.43%, 97.19% and 96.60%, respectively.

scholarly journals Single molecule tracking and analysis framework including theory-predicted parameter settings

2020 ◽  
Author(s):  
Timo Kuhn ◽  
Johannes Hettich ◽  
J. Christof M. Gebhardt
Keyword(s):  
Single Molecule ◽  
Spatial Information ◽  
Reaction Rates ◽  
Data Sets ◽  
Analysis Framework ◽  
Tracking Errors ◽  
Experimental Conditions ◽  
Single Molecule Tracking ◽  
External Software ◽  
User Friendly

AbstractImaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require additional manual steps to analyze whole data sets or to compare different experimental conditions. We report a fast and comprehensive single molecule tracking and analysis framework (TrackIt) to simultaneously process several multi-movie data sets. A user-friendly GUI offers convenient tracking visualization, multiple state-of-the-art analysis procedures, display of results, and data im- and export at different levels to utilize external software tools. We applied our framework to quantify dissociation rates of a transcription factor in the nucleus and found that tracking errors, similar to fluorophore photobleaching, have to be considered for reliable analysis. Accordingly, we developed an algorithm, which accounts for both tracking losses and suggests optimized tracking parameters when evaluating reaction rates. Our versatile and extensible framework facilitates quantitative analysis of single molecule experiments at different experimental conditions.

Locality Adaptive Discriminant Analysis Framework

2021 ◽  
pp. 1-12
Author(s):  
Xuelong Li ◽  
Qi Wang ◽  
Feiping Nie ◽  
Mulin Chen
Keyword(s):  
Discriminant Analysis ◽  
Analysis Framework

scholarly journals Mapping malaria by sharing spatial information between incidence and prevalence data sets

2021 ◽  
Author(s):  
Tim C. D. Lucas ◽  
Anita K. Nandi ◽  
Elisabeth G. Chestnutt ◽  
Katherine A. Twohig ◽  
Suzanne H. Keddie ◽  
...  
Keyword(s):  
Spatial Information ◽  
Data Sets ◽  
Prevalence Data

Kinematic Fixturing With Respect to a Plane Using Contact Sensing

1998 ◽  
Author(s):  
Walter W. Nederbragt ◽  
Bahram Ravani
Keyword(s):  
Spatial Constraint ◽  
Spatial Constraints ◽  
Constraint Equations ◽  
Contact Sensing ◽  
Geometric Elements ◽  
Work Cells

Abstract This paper presents a method for determining the location of geometric elements that compose the external features of referencing fixtures. Since in most applications parts that are handled in robotic work-cells are on a worktable or a floor, this paper focuses on fixture geometries that reside on a plane of known location. The location of the unknown geometric elements are found using contacts to the geometric elements and spatial constraints between the geometric elements. Geometric equations for contacts between lines, planes, points, spheres, and cylinders are derived. Spatial constraint equations are also derived. An algorithm is given for locating the geometric elements that form the fixture. The algorithm uses the contact equations and spatial constraint equations to locate the geometric elements. To illustrate the use of this algorithm, two examples are described in detail.

scholarly journals Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. Sakinah S. Ahmad ◽  
Witold Pedrycz
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Data Sets ◽  
Swarm Optimization

The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

scholarly journals Graph representation learning: a survey

2020 ◽  
Vol 9 ◽  
Author(s):  
Fenxiao Chen ◽  
Yun-Cheng Wang ◽  
Bin Wang ◽  
C.-C. Jay Kuo
Keyword(s):  
Graph Embedding ◽  
Large Data ◽  
Representation Learning ◽  
Graph Representation ◽  
Data Sets ◽  
Graph Data ◽  
Graph Properties ◽  
Wide Range ◽  
Regular Lattices ◽  
Low Dimensional

Abstract Research on graph representation learning has received great attention in recent years since most data in real-world applications come in the form of graphs. High-dimensional graph data are often in irregular forms. They are more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several stat-of-the-art methods against small and large data sets and compare their performance. Finally, potential applications and future directions are presented.

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