Summary of Identifiability Of a Statistical Model with Two Latent Vectors: Importance Of the Dimensionality Relation and Application to Graph Embedding, by Hiroaki Sasaki
Identifiability of a statistical model with two latent vectors: Importance of the dimensionality relation and application to graph embedding
by Hiroaki Sasaki
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel statistical model of two latent vectors with single auxiliary data, generalizing nonlinear independent component analysis (ICA). The model establishes various identifiability conditions, revealing an insightful dimensionality relation among the two latent vectors and auxiliary data. Unlike previous work, the proposed model allows arbitrary dimensional latent vectors, enabling new identifiability conditions. Surprisingly, the indeterminacies of the proposed model have the same permutation and scale recovery as linear ICA under certain conditions. The paper also applies identifiability theory to a statistical model for graph data, leading to an appealing implication: identifiability depends on the maximum value of link weights in graph data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way to understand complex data. They create a model that can learn patterns from data without being told what those patterns are. The model is special because it allows for different sized groups, which helps us understand how these groups relate to each other. This is important because it means we can learn more about the relationships between things in our data. |
Keywords
» Artificial intelligence » Statistical model
