-
Notifications
You must be signed in to change notification settings - Fork 0
/
info.json
24 lines (24 loc) · 1.86 KB
/
info.json
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
{
"abstract": "High dimensional data often contain multiple facets, and several clustering patterns can co-exist under different variable subspaces, also known as the views. While multi-view clustering algorithms were proposed, the uncertainty quantification remains difficult --- a particular challenge is in the high complexity of estimating the cluster assignment probability under each view, and sharing information among views. In this article, we propose an approximate Bayes approach --- treating the similarity matrices generated over the views as rough first-stage estimates for the co-assignment probabilities; in its Kullback-Leibler neighborhood, we obtain a refined low-rank matrix, formed by the pairwise product of simplex coordinates. Interestingly, each simplex coordinate directly encodes the cluster assignment uncertainty. For multi-view clustering, we let each view draw a parameterization from a few candidates, leading to dimension reduction. With high model flexibility, the estimation can be efficiently carried out as a continuous optimization problem, hence enjoys gradient-based computation. The theory establishes the connection of this model to a random partition distribution under multiple views. Compared to single-view clustering approaches, substantially more interpretable results are obtained when clustering brains from a human traumatic brain injury study, using high-dimensional gene expression data.",
"authors": [
"Leo L. Duan"
],
"emails": [
"li.duan@ufl.edu"
],
"extra_links": [
[
"code",
"https://github.com/leoduan/LatentSimplexPosition"
]
],
"id": "19-239",
"issue": 38,
"pages": [
1,
25
],
"title": "Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification",
"volume": 21,
"year": 2020
}