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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Welcome! Call me Nam.
About me
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Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
Subspace clustering for the finite mixture of generalized hyperbolic distributions
Published in Advances in Data Analysis and Classification, 2019
Download here
One line to rule them all: Generating LO-shot soft-label prototypes
Published in 2021 International Joint Conference on Neural Networks (IJCNN), 2021
Download here
Mode merging for the finite mixture of t-distributions
Published in Stat, 2021
Download here
In the pursuit of sparseness: A new rank-preserving penalty for a finite mixture of factor analyzers
Published in Computational Statistics & Data Analysis, 2021
Download here
talks
Subspace clustering for the finite mixture of generalized hyperbolic distributions
Published:
The finite mixture of generalised hyperbolic distributions is a flexible model for clustering, but its large number of parameters for estimation, especially in high dimensions, can make it computationally expensive to work with. In light of this issue, we provide an extension of the subspace clustering technique developed for finite Gaussian mixtures to that of generalised hyperbolic distribution. The methodology will be demonstrated with numerical experiments.
Effect of penalisation on a mixture of factor analysers
Published:
Factor analysers can be used to obtain a parsimonious estimate of component-wise covariance matrices in a finite mixture model. In addition, one could achieve further parsimony in estimated covariance matrix by penalising on the factor loading matrix. However, an increasing magnitude of penalisation coefficient may result in degenerate factor loading estimates, which may have an adverse effect on maximum likelihood estimation of model parameters. To this end, we investigate the effect of penalisation on sparse estimation of parameters in a finite mixture of factor analysers. We also investigate the effect of such estimates in model-based clustering settings.
Finding a regularized common projection in model-based clustering (…or so I thought)
Published:
Suppose that a finite mixture model identified 3 components from a data set. Which variables contributed the most in identifying said components? That was the initial question. In particular, I wanted to find a smaller, rather than larger, number of important variables, since that would make the investigator’s life easier. Contrary to my expectation, the first-devised strategy was not satisfactory, so I had to backtrack a little after much confusion. On that note, I will discuss briefly the problem of finding a regularized common projection for a finite mixture model, and share my experience in backtracking and re-evaluating my approach.
Topics in component merging in model-based clustering
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Model-based clustering models heterogeneous populations within a data set, where each component is often viewed as a cluster. However, the number of components may not always match that of the underlying clusters. In the case of over-estimation, merging some mixture components could help with identifying more informative clusters. In light of this issue, we will discuss some topics related to component merging in model-based clustering.
Why would I need this again?
Published:
(Spoiler: I needed it again.) We encounter in our courses numerous esoteric concepts and techniques, and wonder why we learn them. Once the final exam or project leaves out hand, we bid farewell to most of the course content and move on. We don’t need them again, after all. (Un)fortunately, my research journey so far has proven otherwise. In this talk, I will share this experience in the context of my most recent problem, which is mixture component merging.
Mode merging for non-Gaussian Finite Mixtures
Published:
Finite mixture models can be interpreted as a model representing heterogeneous sub-populations within the whole population. However, more care is needed when associating a mixture component with a cluster, because a mixture model may fit more components than the number of clusters. Modal merging via the mean shift algorithm can help identifysuch multi-component clusters. So far, most of the related works are focused on the Gaussian finite mixture. As the non-Gaussian finite mixtures gain attention, the need to address the component-cluster correspondence issue in these mixture models grows. In light of this issue, we introduce mode merging methods for several non-Gaussian finite mixtures including power-exponential, normal variance mixture and normal variance-mean mixture.
teaching
STAT 230: Probability
Undergraduate course, Department of Statistics and Actuarial Science, University of Waterloo, 2018
Taught a section of STAT 230: Probability in Spring 2018 term. It is an introductory probability course for math majors and other mathematically inclined undergraduate students.