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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 6: AAAI-22 Technical Tracks 6

Identification of Linear Latent Variable Model with Arbitrary Distribution

February 1, 2023

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Authors

Zhengming Chen

Guangdong University of Technology


Feng Xie

Peking University


Jie Qiao

Guangdong University of Technology Peng Cheng Laboratory


Zhifeng Hao

Guangdong University of Technology Shantou University


Kun Zhang

Carnegie Mellon University


Ruichu Cai

Guangdong University of Technology Peng Cheng Laboratory


DOI:

10.1609/aaai.v36i6.20585


Abstract:

An important problem across multiple disciplines is to infer and understand meaningful latent variables. One strategy commonly used is to model the measured variables in terms of the latent variables under suitable assumptions on the connectivity from the latents to the measured (known as measurement model). Furthermore, it might be even more interesting to discover the causal relations among the latent variables (known as structural model). Recently, some methods have been proposed to estimate the structural model by assuming that the noise terms in the measured and latent variables are non-Gaussian. However, they are not suitable when some of the noise terms become Gaussian. To bridge this gap, we investigate the problem of identification of the structural model with arbitrary noise distributions. We provide necessary and sufficient condition under which the structural model is identifiable: it is identifiable iff for each pair of adjacent latent variables Lx, Ly, (1) at least one of Lx and Ly has non-Gaussian noise, or (2) at least one of them has a non-Gaussian ancestor and is not d-separated from the non-Gaussian component of this ancestor by the common causes of Lx and Ly. This identifiability result relaxes the non-Gaussianity requirements to only a (hopefully small) subset of variables, and accordingly elegantly extends the application scope of the structural model. Based on the above identifiability result, we further propose a practical algorithm to learn the structural model. We verify the correctness of the identifiability result and the effectiveness of the proposed method through empirical studies.

Topics: AAAI

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HOW TO CITE:

Zhengming Chen||Feng Xie||Jie Qiao||Zhifeng Hao||Kun Zhang||Ruichu Cai Identification of Linear Latent Variable Model with Arbitrary Distribution Proceedings of the AAAI Conference on Artificial Intelligence (2022) 6350-6357.

Zhengming Chen||Feng Xie||Jie Qiao||Zhifeng Hao||Kun Zhang||Ruichu Cai Identification of Linear Latent Variable Model with Arbitrary Distribution AAAI 2022, 6350-6357.

Zhengming Chen||Feng Xie||Jie Qiao||Zhifeng Hao||Kun Zhang||Ruichu Cai (2022). Identification of Linear Latent Variable Model with Arbitrary Distribution. Proceedings of the AAAI Conference on Artificial Intelligence, 6350-6357.

Zhengming Chen||Feng Xie||Jie Qiao||Zhifeng Hao||Kun Zhang||Ruichu Cai. Identification of Linear Latent Variable Model with Arbitrary Distribution. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.6350-6357.

Zhengming Chen||Feng Xie||Jie Qiao||Zhifeng Hao||Kun Zhang||Ruichu Cai. 2022. Identification of Linear Latent Variable Model with Arbitrary Distribution. "Proceedings of the AAAI Conference on Artificial Intelligence". 6350-6357.

Zhengming Chen||Feng Xie||Jie Qiao||Zhifeng Hao||Kun Zhang||Ruichu Cai. (2022) "Identification of Linear Latent Variable Model with Arbitrary Distribution", Proceedings of the AAAI Conference on Artificial Intelligence, p.6350-6357

Zhengming Chen||Feng Xie||Jie Qiao||Zhifeng Hao||Kun Zhang||Ruichu Cai, "Identification of Linear Latent Variable Model with Arbitrary Distribution", AAAI, p.6350-6357, 2022.

Zhengming Chen||Feng Xie||Jie Qiao||Zhifeng Hao||Kun Zhang||Ruichu Cai. "Identification of Linear Latent Variable Model with Arbitrary Distribution". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.6350-6357.

Zhengming Chen||Feng Xie||Jie Qiao||Zhifeng Hao||Kun Zhang||Ruichu Cai. "Identification of Linear Latent Variable Model with Arbitrary Distribution". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 6350-6357.

Zhengming Chen||Feng Xie||Jie Qiao||Zhifeng Hao||Kun Zhang||Ruichu Cai. Identification of Linear Latent Variable Model with Arbitrary Distribution. AAAI[Internet]. 2022[cited 2023]; 6350-6357.


ISSN: 2374-3468


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