scholarly journals Cyclical monotonicity and the ergodic theorem

2014 ◽  
Vol 35 (3) ◽  
pp. 710-713 ◽  
Author(s):  
MATHIAS BEIGLBÖCK
Keyword(s):  
Ergodic Theorem ◽  
Euclidean Distance ◽  
Reverse Implication ◽  
Squared Euclidean Distance

AbstractIt is well known that optimal transport plans are cyclically monotone. The reverse implication that cyclically monotone transport plans are optimal needs some assumptions and the proof is non-trivial even if the costs are given by the squared euclidean distance on ${ \mathbb{R} }^{n} $. We establish this result as a corollary to the ergodic theorem.

scholarly journals A test for multigroup comparison using partial least squares path modeling

2019 ◽  
Vol 29 (3) ◽  
pp. 464-477 ◽  
Author(s):  
Michael Klesel ◽  
Florian Schuberth ◽  
Jörg Henseler ◽  
Bjoern Niehaves
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Statistical Power ◽  
Euclidean Distance ◽  
Geodesic Distance ◽  
Group Differences ◽  
Path Modeling ◽  
Content Type ◽  
Significance Level ◽  
Squared Euclidean Distance

Purpose People seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can investigate such heterogeneity through multigroup analysis (MGA). In the context of partial least squares path modeling (PLS-PM), MGA is currently applied to perform multiple comparisons of parameters across groups. However, this approach has significant drawbacks: first, the whole model is not considered when comparing groups, and second, the family-wise error rate is higher than the predefined significance level when the groups are indeed homogenous, leading to incorrect conclusions. Against this background, the purpose of this paper is to present and validate new MGA tests, which are applicable in the context of PLS-PM, and to compare their efficacy to existing approaches. Design/methodology/approach The authors propose two tests that adopt the squared Euclidean distance and the geodesic distance to compare the model-implied indicator correlation matrix across groups. The authors employ permutation to obtain the corresponding reference distribution to draw statistical inference about group differences. A Monte Carlo simulation provides insights into the sensitivity and specificity of both permutation tests and their performance, in comparison to existing approaches. Findings Both proposed tests provide a considerable degree of statistical power. However, the test based on the geodesic distance outperforms the test based on the squared Euclidean distance in this regard. Moreover, both proposed tests lead to rejection rates close to the predefined significance level in the case of no group differences. Hence, our proposed tests are more reliable than an uncontrolled repeated comparison approach. Research limitations/implications Current guidelines on MGA in the context of PLS-PM should be extended by applying the proposed tests in an early phase of the analysis. Beyond our initial insights, more research is required to assess the performance of the proposed tests in different situations. Originality/value This paper contributes to the existing PLS-PM literature by proposing two new tests to assess multigroup differences. For the first time, this allows researchers to statistically compare a whole model across groups by applying a single statistical test.

On the partitioning of squared Euclidean distance and its applications in cluster analysis

Psychometrika ◽  
1989 ◽  
Vol 54 (1) ◽  
pp. 9-23 ◽  
Author(s):  
Randy L. Carter ◽  
Robin Morris ◽  
Roger K. Blashfield
Keyword(s):  
Cluster Analysis ◽  
Euclidean Distance ◽  
Squared Euclidean Distance

Rate-Compatible Punctured Ring Convolutional Coded Continuous Phase Modulation

2012 ◽  
Vol 195-196 ◽  
pp. 217-220
Author(s):  
Lei Zhang ◽  
Man Ping Tong ◽  
Hong Bo Wang
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Phase Modulation ◽  
Euclidean Distance ◽  
Convolutional Codes ◽  
Continuous Phase ◽  
Transmission Efficiency ◽  
Continuous Phase Modulation ◽  
Simulation Results ◽  
Squared Euclidean Distance

In this paper, continuous phase modulation (CPM) with rate-compatible punctured ring convolutional codes is investigated. Some typical schemes with maximum normalized minimum squared euclidean distance (NMSED) are searched and given. The performance of bit error rate for rate-compatible punctured ring convolutional coded CPM on AWGN channel is simulated, and simulation results show that this system can provide good performance of bit error rate and variable-rate capabilities. Furthermore, simulation results also prove that the transmission efficiency increases when code rate is decreasing.

EFFECTIVENESS OF ACADEMIC TEACHING IN THE FIELD OF AGRICULTURAL SCIENCES

2018 ◽  
Vol XX (1) ◽  
pp. 104-110 ◽  
Author(s):  
Piotr Pietrzak
Keyword(s):  
Higher Education ◽  
Cluster Analysis ◽  
Pilot Study ◽  
Euclidean Distance ◽  
Public Universities ◽  
Full Time ◽  
Academic Teaching ◽  
Empirical Verification ◽  
Agricultural Sciences ◽  
Squared Euclidean Distance

The paper discusses the effectiveness of teaching in fields representing agricultural sciences. Empirical verification was based on data taken from the Ministry of Science and Higher Education. The research is a pilot study and concerns 1935 graduates of 10 Polish public universities, who obtained a second-cycle full-time studies diploma in 2015. Cluster analysis was performed using Ward’s method and squared Euclidean distance. The conducted procedure allowed to distinguish three clusters of fields differing in level of effectiveness of teaching. In general, the highest effectiveness in the studied group of fields of science was characterized by those that were run through universities located in the capital and cities over 500,000 residents.

scholarly journals TAXONOMIC REVISION OF NIGERIAN SPECIES OF Capsicum L. BASED ON SOME MORPHOLOGICAL CHARACTERS

2021 ◽  
Vol 5 (2) ◽  
pp. 43-46
Author(s):  
Adeyinka O. Adepoju ◽  
Tunde J. Ogunkunle ◽  
Abiola G. Femi-Adepoju
Keyword(s):  
Genetic Variation ◽  
Euclidean Distance ◽  
Germplasm Collection ◽  
Taxonomic Status ◽  
Taxonomic Revision ◽  
Morphological Characters ◽  
Infrageneric Classification ◽  
Reproductive Morphology ◽  
Operational Taxonomic Units ◽  
Squared Euclidean Distance

Species of Capsicum L. are closely related plants whose taxonomic status has remained controversial among different taxonomists. This study was designed to examine the taxonomic status of the species of Capsicum in Nigeria in order to establish the genetic variation between the species for the purpose of identification, as well as review the infrageneric classification (INC) of the members of the genus. Germplasm collection of the seeds of five cultivars of Capsicum were regenerated and nurtured to fruiting. Variations in their vegetative and reproductive morphology were macroscopically evaluated in replicates of 30 individuals per cultivar for each character, which equals 150 samples altogether. The cultivars of each species was hierarchically clustered as operational taxonomic units (OTUs) using Ward’s method with squared Euclidean distance. Artificial key was also constructed for the identification of the species in the genus. The twenty-three (23) morphological characters adopted gave useful insights into the INC of the species and were sufficiently diagnostic of the species as evidenced by the artificial key. Through this study, some light has been shed on the delimitation of species and varieties of the Nigerian Capsicum.

scholarly journals Dynamic Euclidean K-Means Clustering Algorithm in Data Mining

2019 ◽  
Vol 8 (4) ◽  
pp. 11129-11133
Keyword(s):  
Data Mining ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Threshold Value ◽  
Distance Metrics ◽  
Processing Data ◽  
Data Grouping ◽  
Logical Information ◽  
Analyze Data ◽  
Squared Euclidean Distance

Data mining was the practice of processing data in order to derive interesting patterns as well as designs from the system used to analyze data. Grouping was the process of grouping artifacts even though that items in almost the same category are more identical than items in other classes. The existing system main drawbacks are not able to show clear logical information about the market analysis and cannot summarize the strength, weakness, opportunities and threats. Among these clustering is considered as a significant technique to capture the structure of data. Data mining adds to clustering is complicated to retrieve Wide databases with either a variety of different forms of attributes. This includes special specific clustering strategies with Euclidean K-Means grouping process. The power of k-means algorithm is due to its computational efficiency and the nature of ease at which it can be used. In this technique the threshold value is used to determine the information is the same category or even a new team is formed. Proposing an Euclidean K-means algorithm is a necessity. The squared Euclidean distance metric results of the suggested algorithm are tested in this journal experimental results. Distance metrics are used to build reliable features and functionality including grouping for data mining. The simulation process is carried out in MATLAB tool and outperforms the proposed results.

scholarly journals A Feature Space Constraint-Based Method for Change Detection in Heterogeneous Images

2020 ◽  
Vol 12 (18) ◽  
pp. 3057
Author(s):  
Nian Shi ◽  
Keming Chen ◽  
Guangyao Zhou ◽  
Xian Sun
Keyword(s):  
Change Detection ◽  
Space Constraint ◽  
Euclidean Distance ◽  
Detection Method ◽  
Feature Space ◽  
Cross Entropy ◽  
Sar Images ◽  
Feature Spaces ◽  
Gram Matrices ◽  
Squared Euclidean Distance

With the development of remote sensing technologies, change detection in heterogeneous images becomes much more necessary and significant. The main difficulty lies in how to make input heterogeneous images comparable so that the changes can be detected. In this paper, we propose an end-to-end heterogeneous change detection method based on the feature space constraint. First, considering that the input heterogeneous images are in two distinct feature spaces, two encoders with the same structure are used to extract features, respectively. A decoder is used to obtain the change map from the extracted features. Then, the Gram matrices, which include the correlations between features, are calculated to represent different feature spaces, respectively. The squared Euclidean distance between Gram matrices, termed as feature space loss, is used to constrain the extracted features. After that, a combined loss function consisting of the binary cross entropy loss and feature space loss is designed for training the model. Finally, the change detection results between heterogeneous images can be obtained when the model is trained well. The proposed method can constrain the features of two heterogeneous images to the same feature space while keeping their unique features so that the comparability between features can be enhanced and better detection results can be achieved. Experiments on two heterogeneous image datasets consisting of optical and SAR images demonstrate the effectiveness and superiority of the proposed method.

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