Recursive dimensionality reduction using Fisher’s linear discriminant

1998 ◽  
Vol 31 (7) ◽  
pp. 881-888 ◽  
Author(s):  
Wendy L. Poston ◽  
David J. Marchette
Keyword(s):  
Dimensionality Reduction ◽  
Linear Discriminant ◽  
Fisher’S Linear Discriminant

scholarly journals A model for spectroscopic food sample analysis using data sonification

2021 ◽  
Author(s):  
Hsein Kew
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Real Life ◽  
Principal Component ◽  
Relevant Information ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Audio Output ◽  
Dimensionality Reduction Method

AbstractIn this paper, we propose a method to generate an audio output based on spectroscopy data in order to discriminate two classes of data, based on the features of our spectral dataset. To do this, we first perform spectral pre-processing, and then extract features, followed by machine learning, for dimensionality reduction. The features are then mapped to the parameters of a sound synthesiser, as part of the audio processing, so as to generate audio samples in order to compute statistical results and identify important descriptors for the classification of the dataset. To optimise the process, we compare Amplitude Modulation (AM) and Frequency Modulation (FM) synthesis, as applied to two real-life datasets to evaluate the performance of sonification as a method for discriminating data. FM synthesis provides a higher subjective classification accuracy as compared with to AM synthesis. We then further compare the dimensionality reduction method of Principal Component Analysis (PCA) and Linear Discriminant Analysis in order to optimise our sonification algorithm. The results of classification accuracy using FM synthesis as the sound synthesiser and PCA as the dimensionality reduction method yields a mean classification accuracies of 93.81% and 88.57% for the coffee dataset and the fruit puree dataset respectively, and indicate that this spectroscopic analysis model is able to provide relevant information on the spectral data, and most importantly, is able to discriminate accurately between the two spectra and thus provides a complementary tool to supplement current methods.

scholarly journals The analysis of sacrum and coccyx length measured with computerized tomography images depending on sex

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rukiye Sumeyye Bakici ◽  
Zulal Oner ◽  
Serkan Oner
Keyword(s):  
Forensic Anthropology ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Sex Estimation ◽  
Biological Profile ◽  
Linear Discriminant ◽  
Discrimination Rate ◽  
Length Measurements ◽  
The Mean ◽  
Fisher’S Linear Discriminant

Abstract Background Sex estimation is vital in establishing an accurate biological profile from the human skeleton, as sex influences the analysis of other elements in both Physical and Forensic Anthropology and Legal Medicine. The present study was conducted to analyze the sex differences between the sacrum and coccyx length based on the measurements calculated with computed tomography (CT) images. One hundred case images (50 females, 50 males) who were between the ages of 25 and 50 and admitted by the emergency department between September 2018 and June 2019 and underwent CT were included in the study. Eighteen lengths, 4 curvature lengths, and 2 regions were measured in sagittal, coronal and transverse planes with orthogonal adjustment for three times. Results It was stated that the mean anterior and posterior sacral length, anterior and posterior sacrococcygeal length, anterior and posterior sacral curvature length, anterior coccygeal curvature length, sacral area, lengths of transverse lines 1, 2, 3 and 4, sacral first vertebra transverse and sagittal length measurements were longer in males when compared to females (p < 0.05). It was noted that the parameter with the highest discrimination value in the receiver operating characteristic (ROC) analysis was the sacral area (AUC = 0.88/Acc = 0.82). Based on Fisher’s linear discriminant analysis findings, the discrimination rate was 96% for males, 92% for females and the overall discrimination rate was 94%. Conclusions It was concluded that the fourteen parameters that were indicated as significant in the present study could be used in anthropology, Forensic Medicine and Anatomy to predict sex.

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