Low-Rank Characteristic Tensor Density Estimation Part I: Foundations

Article Engineering, Electrical & Electronic

Low-Rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation

Magda Amiridi et al.

Summary: This paper proposes a framework that combines dimensionality reduction with non-parametric density estimation. The proposed model captures the underlying distribution of the input data by designing a nonlinear dimensionality reducing auto-encoder. The model achieves promising results on various tasks and datasets.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2022)

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Article Computer Science, Artificial Intelligence

Sum-Product Networks: A Survey

Raquel Sanchez-Cauce et al.

Summary: A sum-product network is a probabilistic model based on a directed acyclic graph, where terminal nodes represent probability distributions and non-terminal nodes represent convex sums and products of probability distributions. They can be used for building tractable models from data and are applicable to various problem domains.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

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Article Engineering, Electrical & Electronic

Information-Theoretic Feature Selection via Tensor Decomposition and Submodularity

Magda Amiridi et al.

Summary: This paper introduces a method of feature selection through low-rank tensor modeling to mitigate complexity and maximize classification performance. By learning the "principal components" of the joint distribution to avoid the curse of dimensionality, and by using a greedy algorithm to tackle the feature selection problem.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2021)

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Article Computer Science, Theory & Methods