Linear Discriminant Analysis for Large-Scale Data: Application on Text and Image Data

2016 ◽  
Author(s):  
Elhadji Ille Gado Nassara ◽  
Edith Grall-Maes ◽  
Malika Kharouf
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Large Scale ◽  
Image Data ◽  
Text And Image ◽  
Linear Discriminant ◽  
Large Scale Data ◽  
Data Application ◽  
Scale Data

scholarly journals Investigation of Polar Mesospheric Summer Echoes Using Linear Discriminant Analysis

2021 ◽  
Vol 13 (3) ◽  
pp. 522
Author(s):  
Dorota Jozwicki ◽  
Puneet Sharma ◽  
Ingrid Mann
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Image Data ◽  
Atmospheric Conditions ◽  
Very High Frequency ◽  
Vhf Radar ◽  
Linear Discriminant ◽  
Radar Echoes ◽  
Summer Echoes ◽  
Percent Accuracy

Polar Mesospheric Summer Echoes (PMSE) are distinct radar echoes from the Earth’s upper atmosphere between 80 to 90 km altitude that form in layers typically extending only a few km in altitude and often with a wavy structure. The structure is linked to the formation process, which at present is not yet fully understood. Image analysis of PMSE data can help carry out systematic studies to characterize PMSE during different ionospheric and atmospheric conditions. In this paper, we analyze PMSE observations recorded using the European Incoherent SCATter (EISCAT) Very High Frequency (VHF) radar. The collected data comprises of 18 observations from different days. In our analysis, the image data is divided into regions of a fixed size and grouped into three categories: PMSE, ionosphere, and noise. We use statistical features from the image regions and employ Linear Discriminant Analysis (LDA) for classification. Our results suggest that PMSE regions can be distinguished from ionosphere and noise with around 98 percent accuracy.

scholarly journals Song type variations of Louisiana Waterthrush (Parkesia motacilla) and their geographic distributions

2020 ◽  
Author(s):  
W. Ross Silcock ◽  
Shari L. Schwartz ◽  
John U. Carlini ◽  
Stephen J. Dinsmore
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Individual Variation ◽  
Large Scale ◽  
Error Rates ◽  
Western Hemisphere ◽  
Song Type ◽  
Linear Discriminant ◽  
Geographic Distributions ◽  
Song Types

AbstractLouisiana Waterthrush (Parkesia motacilla) is a familiar singer in the Western Hemisphere family Parulidae, yet apparent geographic variations in its song and potentially related causal mechanisms have not received detailed examination in previously published studies. Here, we analyzed song pattern variations of 651 Louisiana Waterthrush singers in audio spectrogram recordings obtained from our field work and publicly accessible bioacoustics archives. Visual and auditory assessment of the introductory note sequence of each song identified three distinct song types (A, B, and C) and 88.3% of the songs were assigned to one of these types. Linear Discriminant Analysis and Random Forest methods were used to verify the assignments and showed strong agreement (>90%) for Type A with slightly less agreement on Types B and C. User error rates (proportion of the Linear Discriminant Analysis classifications that were incorrect) were <10% for Types A and B, but 26% for Type C, while producer error rates (proportion of the song type for which the Linear Discriminant Analysis was incorrect) were >25% for Types A and C, but <5% for Type B. Our findings confirmed in a subset of 87 individuals that most between-individual variation was in the number of notes and note sequence duration while most within-individual variation resulted from the percent of downstrokes. The location of each singer was plotted on a map of the breeding range and results indicated the song types have large-scale discrete geographic distributions that co-occur in some regions but not range-wide. Evaluation of the distributions provided tentative support for a hypothesis that two of the song types may independently exhibit congruence with the geographic extent of Pleistocene glacial boundaries and the third song type may be distinguished by a lack of congruence, but further investigation is needed to elucidate whether the song variations represent subpopulations with three separate evolutionary histories.

scholarly journals Improving Tropical Cyclogenesis Statistical Model Forecasts through the Application of a Neural Network Classifier

2005 ◽  
Vol 20 (6) ◽  
pp. 1073-1083 ◽  
Author(s):  
Christopher C. Hennon ◽  
Caren Marzban ◽  
Jay S. Hobgood
Keyword(s):  
Neural Network ◽  
Discriminant Analysis ◽  
Statistical Model ◽  
Linear Discriminant Analysis ◽  
Large Scale ◽  
Probability Of Detection ◽  
Tropical Cyclogenesis ◽  
Neural Network Classifier ◽  
Linear Discriminant ◽  
Binary Neural Network

Abstract A binary neural network classifier is evaluated against linear discriminant analysis within the framework of a statistical model for forecasting tropical cyclogenesis (TCG). A dataset consisting of potential developing cloud clusters that formed during the 1998–2001 Atlantic hurricane seasons is used in conjunction with eight large-scale predictors of TCG. Each predictor value is calculated at analysis time. The model yields 6–48-h probability forecasts for genesis at 6-h intervals. Results consistently show that the neural network classifier performs comparably to or better than linear discriminant analysis on all performance measures examined, including probability of detection, Heidke skill score, and forecast reliability. Two case studies are presented to investigate model performance and the feasibility of adapting the model to operational forecast use.

scholarly journals Song type variations of Louisiana Waterthrush (Parkesia motacilla) and their geographic distributions

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256385
Author(s):  
W. Ross Silcock ◽  
Shari L. Schwartz ◽  
John U. Carlini ◽  
Stephen J. Dinsmore
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Individual Variation ◽  
Large Scale ◽  
Error Rates ◽  
Western Hemisphere ◽  
Song Type ◽  
Linear Discriminant ◽  
Geographic Distributions ◽  
Song Types

Louisiana Waterthrush (Parkesia motacilla) is a familiar singer in the Western Hemisphere family Parulidae, yet apparent geographic variations in its song and potentially related causal mechanisms have not received detailed examination in previously published studies. Here, we analyzed song pattern variations of 651 Louisiana Waterthrush singers in audio spectrogram recordings obtained from our field work and publicly accessible bioacoustics archives. Visual and auditory assessment of the introductory note sequence of each song identified three distinct song types (A, B, and C) and most of the songs were assigned to one of these types. Linear Discriminant Analysis and Random Forest methods were used to verify the assignments and showed strong agreement for Type A with slightly less agreement on Types B and C. User error rates (proportion of the Linear Discriminant Analysis classifications that were incorrect) were low for Types A and B, and somewhat higher for Type C, while producer error rates (proportion of the song type for which the Linear Discriminant Analysis was incorrect) were somewhat higher for Types A and C than the minimal levels achieved for Type B. Our findings confirmed that most between-individual variation was in the number of notes and note sequence duration while most within-individual variation resulted from the percent of downstrokes. The location of each singer was plotted on a map of the breeding range and results suggested the song types have large-scale discrete geographic distributions that co-occur in some regions but not range-wide. Evaluation of the distributions provided tentative support for a hypothesis that two of the song types may independently exhibit congruence with the geographic extent of Pleistocene glacial boundaries and the third song type may be distinguished by a lack of congruence, but further investigation is needed to elucidate whether the song variations represent subpopulations with three separate evolutionary histories.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data
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