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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.04930 |
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| _version_ | 1866908521074786304 |
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| author | Flores, Philippe Usevich, Konstantin Brie, David |
| author_facet | Flores, Philippe Usevich, Konstantin Brie, David |
| contents | In this article, a Probability Mass Function (PMF) estimation method which tames the curse of dimensionality is proposed. This method, called Partial Coupled Tensor Factorization of 3D marginals or PCTF3D, has for principle to partially couple order-3 data projections -- seen as order-3 tensors -- to obtain a tensor decomposition of the probability mass tensor. The novelty of PCTF3D relies on partial coupling which consists in choosing a subset of 3D marginals. The choice of marginals is then formulated with hypergraphs. After presenting possible coupling strategies, some numerical experiments and an application of the method are proposed. This article is the first of a two-part article. While this first article focuses on a new algorithmic framework for PMF estimation, the second studies uniqueness properties of the model introduced in this article. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04930 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Coupled tensor models for probability mass function estimation: Part I, Principles and algorithms Flores, Philippe Usevich, Konstantin Brie, David Signal Processing In this article, a Probability Mass Function (PMF) estimation method which tames the curse of dimensionality is proposed. This method, called Partial Coupled Tensor Factorization of 3D marginals or PCTF3D, has for principle to partially couple order-3 data projections -- seen as order-3 tensors -- to obtain a tensor decomposition of the probability mass tensor. The novelty of PCTF3D relies on partial coupling which consists in choosing a subset of 3D marginals. The choice of marginals is then formulated with hypergraphs. After presenting possible coupling strategies, some numerical experiments and an application of the method are proposed. This article is the first of a two-part article. While this first article focuses on a new algorithmic framework for PMF estimation, the second studies uniqueness properties of the model introduced in this article. |
| title | Coupled tensor models for probability mass function estimation: Part I, Principles and algorithms |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.04930 |