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Exploiting Multiple Mahalanobis Distance Metrics to Screen Outliers From Analog Product Manufacturing Test Responses
2013
IEEE design & test
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One of the commonly used multivariate metrics for classifying defective devices from non-defective ones is Mahalanobis distance. ...
Multiple Mahalanobis distances are calculated from selected sets of test-response measurements to circumvent this problem. ...
ACKNOWLEDGMENT The authors would like to thank Anne Potzel for the language revision. ...
doi:10.1109/mdt.2012.2206552
fatcat:jdr4k356i5al7hlf57gkom5xxi
An empirical study of predicting software faults with case-based reasoning
2006
Software quality journal
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In addition, the CBR models have better performance than models based on multiple linear regression. ...
We present a software fault prediction modeling approach with case-based reasoning (CBR), a part of the computational intelligence field focusing on automated reasoning processes. ...
Jones and the EMERALD team for collecting the necessary case-study data. We also thank Kehan Gao and Jayanth Rajeevalochanam for their assistance with patient reviews. ...
doi:10.1007/s11219-006-7597-z
fatcat:7qijihzynnaq7nd6kpqep6qx5m
PCA as a Practical Indicator of OPLS-DA Model Reliability
2016
Current Metabolomics
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Methods-A Monte Carlo analysis of PCA group separations and OPLS-DA cross-validation metrics was performed on NMR datasets with statistically significant separations in scores-space. ...
between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. ...
The research was performed in facilities renovated with support from the National Institutes of Health (RR015468-01). ...
doi:10.2174/2213235x04666160613122429
pmid:27547730
pmcid:PMC4990351
fatcat:letwhf62jja5hmqjgq34huhryi
Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors
2010
Forestry (London)
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Furthermore, The LiDAR-based metrics showed major relevance in predicting both response variables examined here. ...
used for plot-level nonparametric predictions of the total volume and biomass using three distance measures of Euclidean, Mahalanobis and Most Similar Neighbour as well as a regression tree-based classifier ...
Crookston (Rocky Mountain Research Station, USDA Forest Service) are appreciated for their technical supports on the application of NN methods. ...
doi:10.1093/forestry/cpq022
fatcat:brc5ft5ttbfadgyqci4pgmg4me
An evaluation of mapped species distribution models used for conservation planning
2005
Environmental Conservation
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, Mahalanobis distance and ecological niche models. ...
Conservation professionals should choose a model and variable set based on the question, the ecology of the species and the availability of requisite data. ...
We thank Mari Wood for allowing us to work with her caribou data. This paper was greatly improved by the constructive comments of three anonymous reviewers. ...
doi:10.1017/s0376892905002171
fatcat:qxudsx2mijb7rnq4qegia4utoi
Correlation-aware Sport Training Evaluation for Players with Trust based on Mahalanobis Distance
2021
IEEE Access
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with trust (abbreviated as CPE MD ) based on Mahalanobis Distance. ...
As Mahalanobis Distance can eliminate the hidden linear correlations among the involved multiple dimensions, we can guarantee the fairness and trust of Mahalanobis Distance-based player training score ...
with trust based on Mahalanobis Distance, i.e., CPE MD . ...
doi:10.1109/access.2021.3114590
fatcat:dsntdmkvwzfmxe7n5vqatjarqq
Automatic Evaluation of Speaker Similarity
[article]
2022
arXiv
pre-print
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For that purpose, we extend the recent work on speaker verification systems and evaluate how different metrics and speaker embeddings models reflect Multiple Stimuli with Hidden Reference and Anchor (MUSHRA ...
Our experiments show that we can train a model to predict speaker similarity MUSHRA scores from speaker embeddings with 0.96 accuracy and significant correlation up to 0.78 Pearson score at the utterance ...
For this purpose, we evaluate several multidimensional metrics, and measure the correlation of distances between speaker embeddings and perceptual scores of speaker similarity according to the Multiple ...
arXiv:2207.00344v1
fatcat:jvnoq6hv4vdkjfzer3i32ujbmm
Comparing a multivariate response Bayesian random effects logistic regression model with a latent variable item response theory model for provider profiling on multiple binary indicators simultaneously
2020
Statistics in Medicine
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Use of this model allows for (i) determining the probability that a hospital has poor performance on a single indicator; (ii) determining the probability that a hospital has poor performance on multiple ...
We compared inferences made using this approach with those obtained using a latent variable item response theory model. ...
a different metric based on the Bayesian multivariate logistic regression model. ...
doi:10.1002/sim.8484
pmid:32043653
fatcat:jtcfletjnfcvbheswujfuy55sa
:{unav)
2012
Empirical Software Engineering
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Prediction models based on software metrics can predict number of faults in software modules. ...
Two-way ANOVA randomized-complete block design models with two blocking variables are designed with average absolute and average relative errors as response variables. ...
and Validation Facility at Fairmont, West ...
doi:10.1023/a:1024424811345
fatcat:gowbcpzl5zdzfdb4aduaa6d7de
Learning Expected Hitting Time Distance
2016
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Most distance metric learning (DML) approaches focus on learning a Mahalanobis metric for measuring distances between examples. ...
However, for particular feature representations, e.g., histogram features like BOW and SPM, Mahalanobis metric could not model the correlations between these features well. ...
can learn a group of local Mahalanobis distances based on fixed metric bases. ...
doi:10.1609/aaai.v30i1.10277
fatcat:4qxisznwhnauhgog5x6gdrxkwi
A Diagnostic Approach to Assess the Quality of Data Splitting in Machine Learning
[article]
2022
arXiv
pre-print
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We associate model robustness with random splitting using a self-defined data-driven distance metric based on the Mahalanobis squared distance between a train set and its corresponding test set. ...
A proposed model is built based on the training data, and then the performance of the model is assessed using test data. ...
Neeraja would like to acknowledge the Samsung Fellowship for this work. Palash Ghosh would like to acknowledge support by the ICMR Centre for Excellence, Grant no. 5/3/8/20/2019-ITR. ...
arXiv:2206.11721v1
fatcat:6ewzwjc4j5drnl6ftxpqdumvfu
Towards a Resilience to Stress Index Based on Physiological Response: A Machine Learning Approach
2021
Sensors
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The benefits of having a metric that measures resilience to stress are multiple; for instance, to the extent that individuals can track their resilience to stress, they can improve their everyday life. ...
, Cluster Validity Index Distance, and Euclidean Distance of Kernel PCA. ...
Acknowledgments: The authors acknowledge the support of the Department of Psychology of Tecnológico de Monterrey for developing the psychophysiological mental stress test, especially to Fresia Hernández ...
doi:10.3390/s21248293
pmid:34960385
pmcid:PMC8705801
fatcat:p5riv2etfncnjd46lhbaiytpw4
GLMdenoise improves multivariate pattern analysis of fMRI data
[article]
2018
bioRxiv
pre-print
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GLMdenoise is a denoising technique for task-based fMRI. ...
in a general linear model (GLM) analysis. ...
(B) GLMdenoise can be combined with cross-validated Mahalanobis 11 distances. ...
doi:10.1101/320838
fatcat:mwc7ggzb6ra6bmodhlfj3pawvm
Image Set Based Face Recognition Using Self-Regularized Non-Negative Coding and Adaptive Distance Metric Learning
2013
IEEE Transactions on Image Processing
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Using the nearest points between a query set and all the gallery sets as well as the active samples used to approximate them, we learn a more discriminative Mahalanobis distance for robust face recognition ...
We propose selfregularized non-negative coding to define between set distance for robust face recognition. ...
Cevikalp for sharing the source code of AHISD/CHISD and providing the LBP features for the Mobo dataset. ...
doi:10.1109/tip.2013.2282996
pmid:24107936
fatcat:befep33hafb5nnsdlm23qipjyq
Neural Decoding with Kernel-Based Metric Learning
2014
Neural Computation
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For instance, a well-suited distance metric enables us to gauge the similarity of neural responses to various stimuli and assess the variability of responses to a repeated stimulusexploratory steps in ...
In studies of the nervous system, the choice of metric for the neural responses is a pivotal assumption. ...
We thank Memming Park, Luis Giraldo Sanchez, and Sohan Seth for their helpful discussions and insight on topics that lead to this work. ...
doi:10.1162/neco_a_00591
pmid:24684447
fatcat:b7h4xu2e2jbu5cr6zfgzafu74i
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