Comparing predictive validity in a community sample: High–dimensionality and traditional domain–and–facet structures of personality variation

2020 ◽  
Vol 34 (6) ◽  
pp. 1120-1137
Author(s):  
Gerard Saucier ◽  
Kathryn Iurino ◽  
Amber Gayle Thalmayer
Keyword(s):  
Personality Assessment ◽  
Community Sample ◽  
High Dimensionality ◽  
Structure Model ◽  
Predictive Capacity ◽  
Personality Psychology ◽  
Measurement Models ◽  
Efficient Route ◽  
Personality Models ◽  
Criterion Variables

Prediction of outcomes is an important way of distinguishing, among personality models, the best from the rest. Prominent previous models have tended to emphasize multiple internally consistent “facet” scales subordinate to a few broad domains. But such an organization of measurement may not be optimal for prediction. Here, we compare the predictive capacity and efficiency of assessments across two types of personality–structure model: conventional structures of facets as found in multiple platforms, and new high–dimensionality structures emphasizing those based on natural–language adjectives, in particular lexicon–based structures of 20, 23, and 28 dimensions. Predictions targeted 12 criterion variables related to health and psychopathology, in a sizeable American community sample. Results tended to favor personality–assessment platforms with (at least) a dozen or two well–selected variables having minimal intercorrelations, without sculpting of these to make them function as indicators of a few broad domains. Unsurprisingly, shorter scales, especially when derived from factor analyses of the personality lexicon, were shown to take a more efficient route to given levels of predictive capacity. Popular 20th–century personality–assessment models set out influential but suboptimal templates, including one that first identifies domains and then facets, which compromise the efficiency of measurement models, at least from a comparative–prediction standpoint. © 2020 European Association of Personality Psychology

scholarly journals RA3 is a reference-guided approach for epigenetic characterization of single cells

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shengquan Chen ◽  
Guanao Yan ◽  
Wenyu Zhang ◽  
Jinzhao Li ◽  
Rui Jiang ◽  
...  
Keyword(s):  
Single Cell ◽  
Computational Analysis ◽  
Reference Data ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Biological Variation ◽  
Superior Performance ◽  
High Dimensionality ◽  
High Degree

AbstractThe recent advancements in single-cell technologies, including single-cell chromatin accessibility sequencing (scCAS), have enabled profiling the epigenetic landscapes for thousands of individual cells. However, the characteristics of scCAS data, including high dimensionality, high degree of sparsity and high technical variation, make the computational analysis challenging. Reference-guided approaches, which utilize the information in existing datasets, may facilitate the analysis of scCAS data. Here, we present RA3 (Reference-guided Approach for the Analysis of single-cell chromatin Accessibility data), which utilizes the information in massive existing bulk chromatin accessibility and annotated scCAS data. RA3 simultaneously models (1) the shared biological variation among scCAS data and the reference data, and (2) the unique biological variation in scCAS data that identifies distinct subpopulations. We show that RA3 achieves superior performance when used on several scCAS datasets, and on references constructed using various approaches. Altogether, these analyses demonstrate the wide applicability of RA3 in analyzing scCAS data.

Handling High-Dimensionality on Software Defect Prediction with FLDA

2021 ◽  
Author(s):  
Rizal Broer Bahaweres ◽  
Ellrica Dewi Herawati Jana ◽  
Irman Hermadi ◽  
Arif Imam Suroso ◽  
Yandra Arkeman
Keyword(s):  
High Dimensionality ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect

scholarly journals Dual Shot Face Detecting using Deep Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 5669-5672
Keyword(s):  
Deep Learning ◽  
High Dimensionality ◽  
Absolute Accuracy ◽  
Robust Method ◽  
Learning Technique ◽  
Benchmark Datasets ◽  
Dual Images ◽  
Preprocessing Technique ◽  
Day By Day ◽  
Feature Extraction Technique

In the paper, we have used a deep learning technique to identify dual faces i.e. nothing but detecting dual shot faces. As the data is emerging day by day with high dimensionality, recognizing dual faces is a major problem. So wasting time on identifying images is like fiddling around. In order to save time and get absolute accuracy we have implemented a fast preprocessing technique named as Convolutional Neural Network (CNN) along with feature extraction technique which is used to knob the relevant features to detect and identify images/faces. By performing this robust method, our intention is to detect dual images in an efficient way. This technique results in decreased feature cardinality and preserves unique efficiency of the data. The experiment is performed on extensive well liked face detecting benchmark datasets, Wider Face and FDDB. CNN with FE demonstrates the results with superiority and the accuracy was in-depth analyzed by CNN classifier.

scholarly journals Ensemble Classification through Random Projections for single-cell RNA-seq data

2020 ◽  
Author(s):  
Aristidis G. Vrahatis ◽  
Sotiris Tasoulis ◽  
Spiros Georgakopoulos ◽  
Vassilis Plagianakos
Keyword(s):  
Single Cell ◽  
Random Projection ◽  
Classification Performance ◽  
Majority Voting ◽  
Ensemble Classification ◽  
High Dimensionality ◽  
Computational Time ◽  
Biomedical Data ◽  
Rna Seq ◽  
Low Dimensional

AbstractNowadays the biomedical data are generated exponentially, creating datasets for analysis with ultra-high dimensionality and complexity. This revolution, which has been caused by recent advents in biotechnologies, has driven to big-data and data-driven computational approaches. An indicative example is the emerging single-cell RNA-sequencing (scRNA-seq) technology, which isolates and measures individual cells. Although scRNA-seq has revolutionized the biotechnology domain, such data computational analysis is a major challenge because of their ultra-high dimensionality and complexity. Following this direction, in this work we study the properties, effectiveness and generalization of the recently proposed MRPV algorithm for single cell RNA-seq data. MRPV is an ensemble classification technique utilizing multiple ultra-low dimensional Random Projected spaces. A given classifier determines the class for each sample for all independent spaces while a majority voting scheme defines their predominant class. We show that Random Projection ensembles offer a platform not only for a low computational time analysis but also for enhancing classification performance. The developed methodologies were applied to four real biomedical high dimensional data from single-cell RNA-seq studies and compared against well-known and similar classification tools. Experimental results showed that based on simplistic tools we can create a computationally fast, simple, yet effective approach for single cell RNA-seq data with ultra-high dimensionality.

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