Latent Structural Categorical Matrix Completion with Application to Quasispecies Analysis
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 inner loop, while the inner loop reconstructs the categorical matrix through tensor factorization, supported by a corresponding theoretical an...
| Authors | Qian Zhang, Meixia Lin |
|---|---|
| Primary category | math.OC |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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