PMID- 33385310
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220506
IS  - 1939-3539 (Electronic)
IS  - 0098-5589 (Linking)
VI  - 44
IP  - 6
DP  - 2022 Jun
TI  - PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models.
PG  - 3197-3211
LID - 10.1109/TPAMI.2020.3048727 [doi]
AB  - We propose a ParametRIc MAnifold Learning (PRIMAL) algorithm for Gaussian 
      mixtures models (GMM), assuming that GMMs lie on or near to a manifold of 
      probability distributions that is generated from a low-dimensional hierarchical 
      latent space through parametric mappings. Inspired by principal component 
      analysis (PCA), the generative processes for priors, means and covariance 
      matrices are modeled by their respective latent space and parametric mapping. 
      Then, the dependencies between latent spaces are captured by a hierarchical 
      latent space by a linear or kernelized mapping. The function parameters and 
      hierarchical latent space are learned by minimizing the reconstruction error 
      between ground-truth GMMs and manifold-generated GMMs, measured by 
      Kullback-Leibler Divergence (KLD). Variational approximation is employed to 
      handle the intractable KLD between GMMs and a variational EM algorithm is derived 
      to optimize the objective function. Experiments on synthetic data, flow cytometry 
      analysis, eye-fixation analysis and topic models show that PRIMAL learns a 
      continuous and interpretable manifold of GMM distributions and achieves a minimum 
      reconstruction error.
FAU - Liu, Ziquan
AU  - Liu Z
FAU - Yu, Lei
AU  - Yu L
FAU - Hsiao, Janet H
AU  - Hsiao JH
FAU - Chan, Antoni B
AU  - Chan AB
LA  - eng
PT  - Journal Article
DEP - 20220505
PL  - United States
TA  - IEEE Trans Pattern Anal Mach Intell
JT  - IEEE transactions on pattern analysis and machine intelligence
JID - 9885960
SB  - IM
EDAT- 2021/01/02 06:00
MHDA- 2021/01/02 06:01
CRDT- 2021/01/01 17:02
PHST- 2021/01/02 06:00 [pubmed]
PHST- 2021/01/02 06:01 [medline]
PHST- 2021/01/01 17:02 [entrez]
AID - 10.1109/TPAMI.2020.3048727 [doi]
PST - ppublish
SO  - IEEE Trans Pattern Anal Mach Intell. 2022 Jun;44(6):3197-3211. doi: 
      10.1109/TPAMI.2020.3048727. Epub 2022 May 5.