A Statistical Approach to High-dimension High-order Spatial-Temporal Data Clustering
Grant Data
Project Title
A Statistical Approach to High-dimension High-order Spatial-Temporal Data Clustering
Principal Investigator
Professor Feng, Long
(Principal Investigator (PI))
Duration
36
Start Date
2023-01-01
Amount
655390
Conference Title
A Statistical Approach to High-dimension High-order Spatial-Temporal Data Clustering
Keywords
1) Spatial-temporal data
2) Clustering
3) Tensor decomposition
4) High-dimensional data
5) Sparse SVD
Discipline
Probability & Statistics
Panel
Physical Sciences (P)
HKU Project Code
21313922
Grant Type
Early Career Scheme (ECS) 2022/23
Funding Year
2022
Status
On-going
Objectives
1 For both matrices and tensors represented spatial-temporal data, develop a unified framework for clustering and spatial regions/temporal periods detection. 2 Develop efficient algorithms for tensor decomposition. The tensor decomposition algorithm will be used to address the high-dimension high-order spatial-temporal data clustering problem. 3 Provide theoretical guarantees for recovering sparse tensor with CP low-rank structure. In particular, convergence results of the alternatively updating algorithm will be developed. 4 Provide theoretical guarantees for the clustering accuracy and region detection consistency. 5 Evaluate the proposed method in a comprehensive simulation study to demonstrate its region detection and clustering accuracy for high-dimension high-order spatial-temporal data. 6 Apply the proposed methods to real fMRI data, using the UK biobank database. Collaborate with general health researchers and validate the detected brain regions associated with various intellectual disabilities. 7 Develop statistical packages for the proposed methods and facilitate psychiatry researchers in analyzing brain imaging data, particularly, MRI and fMRI data.