Linaria argillicola (Plantaginaceae), a new species of L. sect. Supinae from the southern Iberian Peninsula

Phytotaxa ◽  
2018 ◽  
Vol 343 (2) ◽  
pp. 127 ◽  
Author(s):  
ANA JUAN ◽  
GABRIEL BLANCA ◽  
MIGUEL CUETO ◽  
JULIÁN FUENTES ◽  
LLORENÇ SÁEZ
Keyword(s):  
Principal Component Analysis ◽  
New Species ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Endemic Species ◽  
Morphological Differentiation ◽  
Principal Component ◽  
Linear Discriminant ◽  
Wide Tube ◽  
A New Species

A new species of the genus Linaria is described, illustrated and compared with its morphologically closest relatives from L. sect. Supinae: L. accitensis, L. aeruginea, L. tristis and L. badalii. A principal component analysis (PCA), correspondence analysis (CA), and linear discriminant analysis (LDA) were carried out for morphological differentiation. The species is characterized by long abaxial sepals, corolla mostly yellow or rarely reddish with wide tube, wide winged seeds with a tuberculate seminal disc, and a non-continuous fruiting inflorescence. Linaria argillicola is an edapho-endemic species, growing on the marly gypsiferous deposits from the Guadiana Menor river basin, on the border of Granada and Jaén provinces (Andalusia, Spain).

Taxonomic revision of the Limonium latebracteatum group (Plumbaginaceae), with the description of a new species

Phytotaxa ◽  
2018 ◽  
Vol 333 (1) ◽  
pp. 41 ◽  
Author(s):  
JOAQUÍN MORENO ◽  
ALEJANDRO TERRONES ◽  
MARÍA ÁNGELES ALONSO ◽  
ANA JUAN ◽  
MANUEL B. CRESPO
Keyword(s):  
Principal Component Analysis ◽  
New Species ◽  
Discriminant Analysis ◽  
Iberian Peninsula ◽  
Classification Tree ◽  
Principal Component ◽  
Taxonomic Revision ◽  
Linear Discriminant ◽  
Diagnostic Key ◽  
A New Species

Limonium latebracteatum is a plant species from the central and northeastern Iberian Peninsula, characterised by an inner bract wider than long, glaucous leaves, and wide petioles, which belongs to the Limonium delicatulum group. The L. delicatulum group is a complex group formed by around fifteen Iberian and Balearic species, including endemisms with narrow distributions, which is highly diversified in the Mediterranean territories of the Iberian Peninsula. The species in that group are similar each other and occasionally they are not well-delimited morphologically. In this framework, a taxonomic revision of L. latebracteatum and close species in the Iberian Peninsula has been carried out to clarify the taxonomy of L. latebracteatum and L. carpetanicum. This revision has been based on morphological features and supported by ordination analyses such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). In addition, a classification tree was performed to support these analyses. As a result of this study, L. latebracteatum has been separated in two differentspecies: L. latebracteatum and L. admirabile, a new species endemic to Albacete province which is described here. Finally, a diagnostic key is provided for the L. delicatulum group to allow identification of the species in this group.

2D Image Matrix-Based Discriminator

2006 ◽  
pp. 258-286
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Image Projection ◽  
Projection Techniques

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image matrix-based Fisher linear discriminant analysis (IMLDA, 2DLDA). After a brief introduction, we first introduce IMPCA. Then IMLDA technology is given. As a result, we summarize some useful conclusions.

scholarly journals Development of a Piezoelectric-Based Odor Reproduction System

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 870
Author(s):  
Tengteng Wen ◽  
Dehan Luo ◽  
Yongjie Ji ◽  
Pingzhong Zhong
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Piezoelectric Transducers ◽  
Linear Discriminant ◽  
Reproduction System ◽  
Machine Olfaction

Odor reproduction, a branch of machine olfaction, is a technology through which a machine represents various odors by blending several odor sources in different proportions and releases them. In this paper, an odor reproduction system is proposed. The system includes an atomization-based odor dispenser using 16 micro-porous piezoelectric transducers. The authors propose the use of an electronic nose combined with a Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA) model to evaluate the effectiveness of the system. The results indicate that the model can be used to evaluate the system.

scholarly journals Principal Component Analysis Sebagai Ekstraksi Fitur Data Microarray Untuk Deteksi Kanker Berbasis Linear Discriminant Analysis

2019 ◽  
Vol 3 (2) ◽  
pp. 72
Author(s):  
Widi Astuti ◽  
Adiwijaya Adiwijaya
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Microarray Data ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensions ◽  
Linear Discriminant ◽  
Cancer Data ◽  
Colon Cancer Data

Cancer is one of the leading causes of death globally. Early detection of cancer allows better treatment for patients. One method to detect cancer is using microarray data classification. However, microarray data has high dimensions which complicates the classification process. Linear Discriminant Analysis is a classification technique which is easy to implement and has good accuracy. However, Linear Discriminant Analysis has difficulty in handling high dimensional data. Therefore, Principal Component Analysis, a feature extraction technique is used to optimize Linear Discriminant Analysis performance. Based on the results of the study, it was found that usage of Principal Component Analysis increases the accuracy of up to 29.04% and f-1 score by 64.28% for colon cancer data.

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