Learning Latent Variable Models by Pairwise Cluster Comparison

Nuaman Asbeh, Boaz Lerner
Proceedings of the Asian Conference on Machine Learning, PMLR 25:33-48, 2012.

Abstract

Identification of latent variables that govern a problem and the relationships among them given measurements in the observed world are important for causal discovery. This identification can be made by analyzing constraints imposed by the latents in the measurements. We introduce the concept of pairwise cluster comparison PCC to identify causal relationships from clusters and a two-stage algorithm, called LPCC, that learns a latent variable model (LVM) using PCC. First, LPCC learns the exogenous and the collider latents, as well as their observed descendants, by utilizing pairwise comparisons between clusters in the measurement space that may explain latent causes. Second, LPCC learns the non-collider endogenous latents and their children by splitting these latents from their previously learned latent ancestors. LPCC is not limited to linear or latent-tree models and does not make assumptions about the distribution. Using simulated and real-world datasets, we show that LPCC improves accuracy with the sample size, can learn large LVMs, and is accurate in learning compared to state-of-the-art algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-v25-asbeh12, title = {Learning Latent Variable Models by Pairwise Cluster Comparison}, author = {Asbeh, Nuaman and Lerner, Boaz}, booktitle = {Proceedings of the Asian Conference on Machine Learning}, pages = {33--48}, year = {2012}, editor = {Hoi, Steven C. H. and Buntine, Wray}, volume = {25}, series = {Proceedings of Machine Learning Research}, address = {Singapore Management University, Singapore}, month = {04--06 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v25/asbeh12/asbeh12.pdf}, url = {https://proceedings.mlr.press/v25/asbeh12.html}, abstract = {Identification of latent variables that govern a problem and the relationships among them given measurements in the observed world are important for causal discovery. This identification can be made by analyzing constraints imposed by the latents in the measurements. We introduce the concept of pairwise cluster comparison PCC to identify causal relationships from clusters and a two-stage algorithm, called LPCC, that learns a latent variable model (LVM) using PCC. First, LPCC learns the exogenous and the collider latents, as well as their observed descendants, by utilizing pairwise comparisons between clusters in the measurement space that may explain latent causes. Second, LPCC learns the non-collider endogenous latents and their children by splitting these latents from their previously learned latent ancestors. LPCC is not limited to linear or latent-tree models and does not make assumptions about the distribution. Using simulated and real-world datasets, we show that LPCC improves accuracy with the sample size, can learn large LVMs, and is accurate in learning compared to state-of-the-art algorithms.} }
Endnote
%0 Conference Paper %T Learning Latent Variable Models by Pairwise Cluster Comparison %A Nuaman Asbeh %A Boaz Lerner %B Proceedings of the Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2012 %E Steven C. H. Hoi %E Wray Buntine %F pmlr-v25-asbeh12 %I PMLR %P 33--48 %U https://proceedings.mlr.press/v25/asbeh12.html %V 25 %X Identification of latent variables that govern a problem and the relationships among them given measurements in the observed world are important for causal discovery. This identification can be made by analyzing constraints imposed by the latents in the measurements. We introduce the concept of pairwise cluster comparison PCC to identify causal relationships from clusters and a two-stage algorithm, called LPCC, that learns a latent variable model (LVM) using PCC. First, LPCC learns the exogenous and the collider latents, as well as their observed descendants, by utilizing pairwise comparisons between clusters in the measurement space that may explain latent causes. Second, LPCC learns the non-collider endogenous latents and their children by splitting these latents from their previously learned latent ancestors. LPCC is not limited to linear or latent-tree models and does not make assumptions about the distribution. Using simulated and real-world datasets, we show that LPCC improves accuracy with the sample size, can learn large LVMs, and is accurate in learning compared to state-of-the-art algorithms.
RIS
TY - CPAPER TI - Learning Latent Variable Models by Pairwise Cluster Comparison AU - Nuaman Asbeh AU - Boaz Lerner BT - Proceedings of the Asian Conference on Machine Learning DA - 2012/11/17 ED - Steven C. H. Hoi ED - Wray Buntine ID - pmlr-v25-asbeh12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 25 SP - 33 EP - 48 L1 - http://proceedings.mlr.press/v25/asbeh12/asbeh12.pdf UR - https://proceedings.mlr.press/v25/asbeh12.html AB - Identification of latent variables that govern a problem and the relationships among them given measurements in the observed world are important for causal discovery. This identification can be made by analyzing constraints imposed by the latents in the measurements. We introduce the concept of pairwise cluster comparison PCC to identify causal relationships from clusters and a two-stage algorithm, called LPCC, that learns a latent variable model (LVM) using PCC. First, LPCC learns the exogenous and the collider latents, as well as their observed descendants, by utilizing pairwise comparisons between clusters in the measurement space that may explain latent causes. Second, LPCC learns the non-collider endogenous latents and their children by splitting these latents from their previously learned latent ancestors. LPCC is not limited to linear or latent-tree models and does not make assumptions about the distribution. Using simulated and real-world datasets, we show that LPCC improves accuracy with the sample size, can learn large LVMs, and is accurate in learning compared to state-of-the-art algorithms. ER -
APA
Asbeh, N. & Lerner, B.. (2012). Learning Latent Variable Models by Pairwise Cluster Comparison. Proceedings of the Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 25:33-48 Available from https://proceedings.mlr.press/v25/asbeh12.html.

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