Hierarchical clustering of observations and features in high-dimensional data
department or school: Science, Faculty of
department or school: Statistics, Department of
data provider: DSpace
degree campus: UBCV
creator: Zhang, Hongyang (Fred)
issued: 2017-08-16T16:15:03Z
issued: 2017
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: In this thesis, we present new developments of hierarchical clustering in high-dimensional data. We consider different use cases of hierarchical clustering, namely, clustering observations for exploratory analysis and clustering high-dimensional features for adaptive feature grouping and ensembling. We first focus on the clustering of observations. In high-dimensional data, the existence of potential noise features and outliers poses unique challenges to the existing hierarchical clustering techniques. We propose the Robust Sparse Hierarchical Clustering (RSHC) and the Multi-rank Sparse Hierarchical Clustering (MrSHC) to address these challenges. We show that via robust feature selection techniques, both RSHC and MrSHC can handle the potential existence of noise features and outliers in high-dimensional data and result in better clustering accuracy and interpretation comparing to the existing hierarchical clustering methods. We then consider clustering of features in high-dimensional data. We propose a new hierarchical clustering technique to adaptively divide the large number of features into subgroups called Regression Phalanxes. Features in the same Regression Phalanx work well together as predictors in a pre-defined regression model. Then models built on different Regression Phalanxes are considered for further ensembling. We show that the ensemble of Regression Phalanxes resulting from the hierarchical clustering produces further gains in prediction accuracy when applied to an effective method like Lasso or Random Forests.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/62648
note: Hierarchical Clustering of Observations and Features inHigh-dimensional DatabyHongyang (Fred) ZhangM.Sc., The University of British Columbia, 2012A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDoctor of PhilosophyinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Statistics)The University of British Columbia(Vancouver)August 2017c© Hongyang (Fred) Zhang, 2017AbstractIn this thesis, we present new developments of hierarchical clustering in high-dimensional data. We consider different use cases of hierarchical clustering, namely,clustering observations for exploratory analysis and clustering high-dimensionalfeatures for adaptive feature grouping and ensembling.We first focus on the clustering of observations. In high-dimensional data,the existence of potential noise features and outliers poses unique challenges tothe existing hierarchical clustering techniques. We propose the Robust Sparse Hi-erarchical Clustering (RSHC) and the Multi-rank Sparse Hierarchical Clustering(MrSHC) to address these challenges. We show that via robust feature selectiontechniques, both RSHC and MrSHC can handle the potential existence of noisefeatures and outliers in high-dimensional data and result in better clustering accu-racy and interpretation comparing to the existing hierarchical clustering methods.We then consider clustering of features in high-dimensional data. We proposea new hierarchical clustering technique to adaptively divide the large number offeatures into subgroups called Regression Phalanxes. Features in the same Regres-sion Phalanx work well together as predictors in a pre-defined regression model.Then models built on different Regression Phalanxes are considered for further en-sembling. We show that the ensemble of Regression Phalanxes resulting from thehierarchical clustering produces further gains in prediction accuracy when appliedto an effective method like Lasso or Random Forests.iiLay SummaryThere has been a surge in the number of high-…
has type: Thesis/Dissertation
date issued: 2017-09
is shown at: 10.14288/1.0354395
language: eng
degree discipline: Statistics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/62648