scholarly journals Geometric consistency of principal component scores for high‐dimensional mixture models and its application

2019 ◽  
Vol 47 (3) ◽  
pp. 899-921
Author(s):  
Kazuyoshi Yata ◽  
Makoto Aoshima
Keyword(s):  
Mixture Models ◽  
Principal Component ◽  
High Dimensional ◽  
Geometric Consistency ◽  
Component Scores

scholarly journals Convergence and prediction of principal component scores in high-dimensional settings

2010 ◽  
Vol 38 (6) ◽  
pp. 3605-3629 ◽  
Author(s):  
Seunggeun Lee ◽  
Fei Zou ◽  
Fred A. Wright
Keyword(s):  
Principal Component ◽  
High Dimensional ◽  
Component Scores

Using Principal Component Scores to Enhance the Validity and Reliability of Big Five Personality Measures

2017 ◽  
Vol 38 (2) ◽  
pp. 83-93
Author(s):  
Jeffrey M. Cucina ◽  
Nicholas L. Vasilopoulos ◽  
Arwen H. DeCostanza
Keyword(s):  
Big Five ◽  
Discriminant Validity ◽  
Reliability And Validity ◽  
Principal Component ◽  
Big Five Personality ◽  
Validity And Reliability ◽  
Scoring Method ◽  
Five Factors ◽  
Big Five Factors ◽  
Component Scores

Abstract. Varimax rotated principal component scores (VRPCS) have previously been offered as a possible solution to the non-orthogonality of scores for the Big Five factors. However, few researchers have examined the reliability and validity of VRPCS. To address this gap, we use a lab study and a field study to investigate whether using VRPCS increase orthogonality, reliability, and criterion-related validity. Compared to the traditional unit-weighting scoring method, the use of VRPCS enhanced the reliability and discriminant validity of the Big Five factors, although there was little improvement in criterion-related validity. Results are discussed in terms of the benefit of using VRPCS instead of traditional unit-weighted sum scores.

scholarly journals Small-variance asymptotics for non-parametric online robot learning

2018 ◽  
Vol 38 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Ajay Kumar Tanwani ◽  
Sylvain Calinon
Keyword(s):  
Mixture Models ◽  
Dirichlet Process ◽  
Large Scale ◽  
Principal Component ◽  
Small Variance ◽  
Remote Manipulation ◽  
State Duration ◽  
Duration Information ◽  
Low Dimensional ◽  
Non Parametric

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-variance asymptotics. The analysis yields a scalable online sequence clustering (SOSC) algorithm that is non-parametric in the number of clusters and the subspace dimension of each cluster. SOSC groups the new datapoint in low-dimensional subspaces by online inference in a non-parametric mixture of probabilistic principal component analyzers (MPPCA) based on a Dirichlet process, and captures the state transition and state duration information online in a hidden semi-Markov model (HSMM) based on a hierarchical Dirichlet process. A task-parameterized formulation of our approach autonomously adapts the model to changing environmental situations during manipulation. We apply the algorithm in a teleoperation setting to recognize the intention of the operator and remotely adjust the movement of the robot using the learned model. The generative model is used to synthesize both time-independent and time-dependent behaviors by relying on the principles of shared and autonomous control. Experiments with the Baxter robot yield parsimonious clusters that adapt online with new demonstrations and assist the operator in performing remote manipulation tasks.

scholarly journals High-Dimensional Mixture Models for Unsupervised Image Denoising (HDMI)

2018 ◽  
Vol 11 (4) ◽  
pp. 2815-2846 ◽  
Author(s):  
Antoine Houdard ◽  
Charles Bouveyron ◽  
Julie Delon
Keyword(s):  
Mixture Models ◽  
Image Denoising ◽  
High Dimensional
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