scholarly journals A Novel Eye Movement Data Transformation Technique that Preserves Temporal Information: A Demonstration in a Face Processing Task

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2377 ◽  
Author(s):  
Michał Król ◽  
Magdalena Ewa Król
Keyword(s):  
Eye Movement ◽  
Spatial Information ◽  
Dimensional Space ◽  
Pairwise Comparison ◽  
Experimental Manipulation ◽  
Movement Data ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Human Faces

Existing research has shown that human eye-movement data conveys rich information about underlying mental processes, and that the latter may be inferred from the former. However, most related studies rely on spatial information about which different areas of visual stimuli were looked at, without considering the order in which this occurred. Although powerful algorithms for making pairwise comparisons between eye-movement sequences (scanpaths) exist, the problem is how to compare two groups of scanpaths, e.g., those registered with vs. without an experimental manipulation in place, rather than individual scanpaths. Here, we propose that the problem might be solved by projecting a scanpath similarity matrix, obtained via a pairwise comparison algorithm, to a lower-dimensional space (the comparison and dimensionality-reduction techniques we use are ScanMatch and t-SNE). The resulting distributions of low-dimensional vectors representing individual scanpaths can be statistically compared. To assess if the differences result from temporal scanpath features, we propose to statistically compare the cross-validated accuracies of two classifiers predicting group membership: (1) based exclusively on spatial metrics; (2) based additionally on the obtained scanpath representation vectors. To illustrate, we compare autistic vs. typically-developing individuals looking at human faces during a lab experiment and find significant differences in temporal scanpath features.

TIME SERIES FOR FAULT DETECTION IN AN INDUSTRIAL PILOT PLANT

2012 ◽  
Vol 26 (25) ◽  
pp. 1246003
Author(s):  
ANTONIO MORÁN ◽  
JUAN J. FUERTES ◽  
SERAFÍN ALONSO ◽  
CARLOS DEL CANTO ◽  
MANUEL DOMÍNGUEZ
Keyword(s):  
Time Series ◽  
Dimensional Space ◽  
Industrial Processes ◽  
Self Organizing Map ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Analysis Of Time Series

Forecasting the evolution of industrial processes can be useful to discover faults. Several techniques based on analysis of time series are used to forecast the evolution of certain critical variables; however, the amount of variables makes difficult the analysis. In this way, the use of dimensionality reduction techniques such as the SOM (Self-Organizing Map) allows us to work with less data to determine the evolution of the process. SOM is a data mining technique widely used for supervision and monitoring. Since the SOM is projects data from a high dimensional space into a 2-D, the SOM reduces the number of variables. Thus, time series with the variables of the low dimensional projection can be created to make easier the prediction of future values in order to detect faults.

scholarly journals Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics

2020 ◽  
Author(s):  
Gregory Kiar ◽  
Yohan Chatelain ◽  
Ali Salari ◽  
Alan C. Evans ◽  
Tristan Glatard
Keyword(s):  
Monte Carlo ◽  
Data Augmentation ◽  
Learning Models ◽  
Reduction Techniques ◽  
Numerical Noise ◽  
Numerical Instabilities ◽  
Dimensionality Reduction Techniques ◽  
Improved Performance ◽  
Low Dimensional ◽  
Machine Learning Models

AbstractMachine learning models are commonly applied to human brain imaging datasets in an effort to associate function or structure with behaviour, health, or other individual phenotypes. Such models often rely on low-dimensional maps generated by complex processing pipelines. However, the numerical instabilities inherent to pipelines limit the fidelity of these maps and introduce computational bias. Monte Carlo Arithmetic, a technique for introducing controlled amounts of numerical noise, was used to perturb a structural connectome estimation pipeline, ultimately producing a range of plausible networks for each sample. The variability in the perturbed networks was captured in an augmented dataset, which was then used for an age classification task. We found that resampling brain networks across a series of such numerically perturbed outcomes led to improved performance in all tested classifiers, preprocessing strategies, and dimensionality reduction techniques. Importantly, we find that this benefit does not hinge on a large number of perturbations, suggesting that even minimally perturbing a dataset adds meaningful variance which can be captured in the subsequently designed models.

scholarly journals Comparative Study of Dimensionality Reduction Techniques for Spectral–Temporal Data

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Shingchern D. You ◽  
Ming-Jen Hung
Keyword(s):  
Principal Component ◽  
Component Analysis ◽  
Identification Accuracy ◽  
Temporal Data ◽  
Reduction Techniques ◽  
Temporal Features ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Low Dimensional Features ◽  
Motion Picture Expert Group

This paper studies the use of three different approaches to reduce the dimensionality of a type of spectral–temporal features, called motion picture expert group (MPEG)-7 audio signature descriptors (ASD). The studied approaches include principal component analysis (PCA), independent component analysis (ICA), and factor analysis (FA). These approaches are applied to ASD features obtained from audio items with or without distortion. These low-dimensional features are used as queries to a dataset containing low-dimensional features extracted from undistorted items. Doing so, we may investigate the distortion-resistant capability of each approach. The experimental results show that features obtained by the ICA or FA reduction approaches have higher identification accuracy than the PCA approach for moderately distorted items. Therefore, to extract features from distorted items, ICA or FA approaches should also be considered in addition to the PCA approach.

scholarly journals Performance Analysis of Dimensionality Reduction Techniques in the Context of Clustering

2019 ◽  
Vol 8 (S3) ◽  
pp. 66-71
Author(s):  
T. Sudha ◽  
P. Nagendra Kumar
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
High Dimensional Data ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Probabilistic Principal Component Analysis

Data mining is one of the major areas of research. Clustering is one of the main functionalities of datamining. High dimensionality is one of the main issues of clustering and Dimensionality reduction can be used as a solution to this problem. The present work makes a comparative study of dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis in the context of clustering. High dimensional data have been reduced to low dimensional data using dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis. Cluster analysis has been performed on the high dimensional data as well as the low dimensional data sets obtained through t-distributed stochastic neighbour embedding and Probabilistic principal component analysis with varying number of clusters. Mean squared error; time and space have been considered as parameters for comparison. The results obtained show that time taken to convert the high dimensional data into low dimensional data using probabilistic principal component analysis is higher than the time taken to convert the high dimensional data into low dimensional data using t-distributed stochastic neighbour embedding.The space required by the data set reduced through Probabilistic principal component analysis is less than the storage space required by the data set reduced through t-distributed stochastic neighbour embedding.

Product Platform Selection in Lower-Dimensional Manifold Spaces

2009 ◽  
Author(s):  
Madan Mohan Dabbeeru ◽  
Amitabha Mukerjee
Keyword(s):  
Design Space ◽  
User Requirements ◽  
Dimensional Manifold ◽  
Portfolio Decisions ◽  
Reduction Techniques ◽  
Design Variables ◽  
Discrete Design Variables ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Lower Dimensional

Product portfolios need to present the widest coverage of user requirements with minimal product diversity. User requirements may vary along multiple performance measures, comprising the objective space, whereas the design variables constitute the design space, which is usually far higher in dimensionality. Here we consider the set of possible performances of interest to the user, and use multi-objective optimization to identify the non-domination or the pareto-front. The designs lying along this front are mapped to the design space; we show that these “good designs” are often restricted to a much lower-dimensional manifold, resulting in significant conceptual and computational efficiency. These non-dominated designs are then clustered in the design space in an unsupervised manner to obtain candidate product groupings which the designer may inspect to arrive at portfolio decisions. With help of dimensionality reduction techniques, we show how these clusters in low-dimensional manifolds embedded in the high-dimensional design space. We demonstrate this process on two different designs (springs and electric motors), involving both continuous and discrete design variables.

scholarly journals Spatial interaction of electroencephalography signal during epileptic seizure on flat electroencephalography

2014 ◽  
Vol 9 (4) ◽  
Author(s):  
Goh Chien Yong ◽  
Tahir Ahmad ◽  
Normah Maan
Keyword(s):  
Gravity Model ◽  
Epileptic Seizure ◽  
Spatial Information ◽  
Dimensional Space ◽  
Spatial Interaction ◽  
Computational Modelling ◽  
Interaction Model ◽  
Sharp Waves ◽  
Low Dimensional ◽  
The Brain

Electroencephalography (EEG) is one of the fields in clinical neurophysiology, which is a recording of the electrical activity of the brain from the scalp. One of the major roles of EEG is as an aid to diagnose epilepsy. Abnormal patterns such as spikes, sharp waves and, spikes and wave complexes can be seen. It is important to extract spatial information about the dynamics from a few observations of this recorded signal regardless where EEG sensors are located. A developed method by Theoretical & Computational Modelling for Complex System (TCM), UTM, namely Flat EEG for mapping high dimensional signal into a low dimensional space will be used as platforms to analyse EEG signal spatially during epileptic seizure. The spatial interactions among clusters are identified through spatial interaction model, namely gravity model. This paper also reveals that gravity model used is a measure.

scholarly journals scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

2021 ◽  
Author(s):  
Dongyuan Song ◽  
Kexin Aileen Li ◽  
Zachary Hemminger ◽  
Roy Wollman ◽  
Jingyi Jessica Li
Keyword(s):  
Single Cell ◽  
Gene Selection ◽  
Spatial Information ◽  
Dimensional Space ◽  
Single Cells ◽  
High Sensitivity ◽  
Cell Types ◽  
Gene Profiling ◽  
Selection Methods ◽  
Low Dimensional

AbstractSingle-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity, and extra (e.g., spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. Here we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and cell-type annotation on targeted gene profiling data.

scholarly journals Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoxiao Sun ◽  
Yiwen Liu ◽  
Lingling An
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Dimensional Space ◽  
Essential Feature ◽  
Empirical Studies ◽  
Expression Patterns ◽  
Cell Types ◽  
Stochastic Gradient Descent ◽  
Reduction Techniques ◽  
Low Dimensional

AbstractSingle-cell RNA sequencing (scRNA-seq) technologies allow researchers to uncover the biological states of a single cell at high resolution. For computational efficiency and easy visualization, dimensionality reduction is necessary to capture gene expression patterns in low-dimensional space. Here we propose an ensemble method for simultaneous dimensionality reduction and feature gene extraction (EDGE) of scRNA-seq data. Different from existing dimensionality reduction techniques, the proposed method implements an ensemble learning scheme that utilizes massive weak learners for an accurate similarity search. Based on the similarity matrix constructed by those weak learners, the low-dimensional embedding of the data is estimated and optimized through spectral embedding and stochastic gradient descent. Comprehensive simulation and empirical studies show that EDGE is well suited for searching for meaningful organization of cells, detecting rare cell types, and identifying essential feature genes associated with certain cell types.

scholarly journals Using social face-space to estimate social variables in real and artificial faces

2019 ◽  
Author(s):  
Benjamin Balas ◽  
Josselyn Thrash
Keyword(s):  
Dimensional Space ◽  
Social Characteristics ◽  
Social Variables ◽  
Social Judgments ◽  
Social Evaluation ◽  
Face Space ◽  
Wide Range ◽  
Low Dimensional ◽  
Human Faces ◽  
Judgment Data

Observers estimate a range of social characteristics from images of human faces. An important unifying framework for these judgments is the observation that a low-dimensional social face-space based on perceived valence and dominance captures most of the variance across a wide range of social evaluation judgments. However, it is not known whether or not this low-dimensional space can be used to infer the outcome of new social judgments. Further, the extent to which such social inference may differ across real and computer-generated faces is also unknown. We addess both of these issues by recovering valence/dominance axes from social judgments made to real and artificial faces, then attempt to use these coordinates to predict independent social judgment data obtained from new human observers. We find that above-chance performance can be achieved, though performance appears to be better with artificial faces than real ones.

Age Differences in Eye Movements During Reading: Degenerative Problems or Compensatory Strategy?

2019 ◽  
Vol 24 (4) ◽  
pp. 297-311
Author(s):  
José David Moreno ◽  
José A. León ◽  
Lorena A. M. Arnal ◽  
Juan Botella
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Meta Analysis ◽  
Age Groups ◽  
Behavioral Changes ◽  
Aging Effects ◽  
Movement Data ◽  
Sentence Reading ◽  
The Difference ◽  
Eye Movements During Reading

Abstract. We report the results of a meta-analysis of 22 experiments comparing the eye movement data obtained from young ( Mage = 21 years) and old ( Mage = 73 years) readers. The data included six eye movement measures (mean gaze duration, mean fixation duration, total sentence reading time, mean number of fixations, mean number of regressions, and mean length of progressive saccade eye movements). Estimates were obtained of the typified mean difference, d, between the age groups in all six measures. The results showed positive combined effect size estimates in favor of the young adult group (between 0.54 and 3.66 in all measures), although the difference for the mean number of fixations was not significant. Young adults make in a systematic way, shorter gazes, fewer regressions, and shorter saccadic movements during reading than older adults, and they also read faster. The meta-analysis results confirm statistically the most common patterns observed in previous research; therefore, eye movements seem to be a useful tool to measure behavioral changes due to the aging process. Moreover, these results do not allow us to discard either of the two main hypotheses assessed for explaining the observed aging effects, namely neural degenerative problems and the adoption of compensatory strategies.

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