An Efficient EMST Algorithm for Clustering Very High-Dimensional Sparse Feature Vectors

2019 ◽  
pp. 153-176
Author(s):  
Xiaochun Wang ◽  
Xiali Wang ◽  
Don Mitchell Wilkes
Keyword(s):  
High Dimensional ◽  
Feature Vectors ◽  
Very High

Extensions to Quantile Regression Forests for Very High-Dimensional Data

2014 ◽  
pp. 247-258 ◽  
Author(s):  
Nguyen Thanh Tung ◽  
Joshua Zhexue Huang ◽  
Imran Khan ◽  
Mark Junjie Li ◽  
Graham Williams
Keyword(s):  
Quantile Regression ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Very High

Indexing very high-dimensional sparse and quasi-sparse vectors for similarity searches

2001 ◽  
Vol 9 (4) ◽  
pp. 344-361 ◽  
Author(s):  
Changzhou Wang ◽  
X. Sean Wang
Keyword(s):  
High Dimensional ◽  
Similarity Searches ◽  
Very High

scholarly journals Can High-Dimensional Questionnaires Resolve the Ipsativity Issue of Forced-Choice Response Formats?

2020 ◽  
pp. 001316442093486
Author(s):  
Niklas Schulte ◽  
Heinz Holling ◽  
Paul-Christian Bürkner
Keyword(s):  
Rating Scales ◽  
Forced Choice ◽  
High Dimensional ◽  
Choice Response ◽  
Scoring Methods ◽  
High Stakes ◽  
Irt Models ◽  
Response Formats ◽  
Response Biases ◽  
Very High

Forced-choice questionnaires can prevent faking and other response biases typically associated with rating scales. However, the derived trait scores are often unreliable and ipsative, making interindividual comparisons in high-stakes situations impossible. Several studies suggest that these problems vanish if the number of measured traits is high. To determine the necessary number of traits under varying sample sizes, factor loadings, and intertrait correlations, simulations were performed for the two most widely used scoring methods, namely the classical (ipsative) approach and Thurstonian item response theory (IRT) models. Results demonstrate that while especially Thurstonian IRT models perform well under ideal conditions, both methods yield insufficient reliabilities in most conditions resembling applied contexts. Moreover, not only the classical estimates but also the Thurstonian IRT estimates for questionnaires with equally keyed items remain (partially) ipsative, even when the number of traits is very high (i.e., 30). This result not only questions earlier assumptions regarding the use of classical scores in high-dimensional questionnaires, but it also raises doubts about many validation studies on Thurstonian IRT models because correlations of (partially) ipsative scores with external criteria cannot be interpreted in a usual way.

scholarly journals Optimal properties of centroid-based classifiers for very high-dimensional data

2010 ◽  
Vol 38 (2) ◽  
pp. 1071-1093 ◽  
Author(s):  
Peter Hall ◽  
Tung Pham
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Very High
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