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Exploiting Multiple Mahalanobis Distance Metrics to Screen Outliers From Analog Product Manufacturing Test Responses

Shaji Krishnan, Hans G. Kerkhoff
2013 IEEE design & test  
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

Taghi M. Khoshgoftaar, Naeem Seliya, Nandini Sundaresh
2006 Software quality journal  
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

Bradley Worley, Robert Powers
2016 Current Metabolomics  
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

H. Latifi, A. Nothdurft, B. Koch
2010 Forestry (London)  
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

CHRIS J. JOHNSON, MICHAEL P. GILLINGHAM
2005 Environmental Conservation  
, 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

Tengfei Fan, Shengli Tian, Zhichen Hu, Xintong Fan, Sifeng Wang
2021 IEEE Access  
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]

Deja Kamil, Sanchez Ariadna, Roth Julian, Cotescu Marius
2022 arXiv   pre-print
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

Peter C. Austin, Douglas S. Lee, George Leckie
2020 Statistics in Medicine  
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

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Taghi M. Khoshgoftaar, Naeem Seliya
2012 Empirical Software Engineering  
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

De-Chuan Zhan, Peng Hu, Zui Chu, Zhi-Hua Zhou
2016 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
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]

Eklavya Jain, J. Neeraja, Buddhananda Banerjee, Palash Ghosh
2022 arXiv   pre-print
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

Ramon E. Diaz-Ramos, Daniela A. Gomez-Cravioto, Luis A. Trejo, Carlos Figueroa López, Miguel Angel Medina-Pérez
2021 Sensors  
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]

Ian Charest, Nikolaus Kriegeskorte, Kendrick Kay
2018 bioRxiv   pre-print
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

Ajmal Mian, Yiqun Hu, Richard Hartley, Robyn Owens
2013 IEEE Transactions on Image Processing  
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

Austin J. Brockmeier, John S. Choi, Evan G. Kriminger, Joseph T. Francis, Jose C. Principe
2014 Neural Computation  
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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