arXiv:2606.08188v1 Announce Type: cross Abstract: Matrix completion has been extensively studied for real-valued data, but existing methods are often limited in handling categorical variables. We propose LCMC, a double-loop optimization framework for categorical matrix completion via latent factorization based on a binary tensor representation. In this setting, each categorical entry is encoded as a one-hot vector along a third tensor mode, thereby preserving its discrete, non-ordinal nature. The outer loop adaptively estimates the latent dimension by iteratively updating it with feedback from
The paper leverages recent advancements in optimization techniques and AI to address a long-standing challenge in handling categorical data, especially relevant for increasing complexity in biological and AI datasets.
This development proposes a novel method for more accurately analyzing categorical data, which is crucial for advanced machine learning applications and understanding complex biological systems like quasispecies.
Current matrix completion methods are often limited in handling categorical variables. This research introduces LCMC, a double-loop optimization framework for categorical matrix completion via latent factorization based on a binary tensor representation. This preserves its discrete, non-ordinal nature.
- · AI researchers
- · Bioinformatics
- · Drug discovery
- · Data scientists
- · Traditional statistical methods
- · Inefficient data analysis techniques
More accurate and efficient analysis of complex categorical datasets, particularly in biology and machine learning.
Accelerated discovery and understanding in fields dominated by categorical data, such as genomics, epidemiological modeling, and AI model interpretability.
Enhanced ability to model and predict the evolution of complex systems, potentially leading to breakthroughs in vaccine development or disease management.
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Read at arXiv cs.LG