Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms
This work addresses structured dictionary learning for tensor data, which is an incremental improvement in a domain-specific area of machine learning.
The paper tackles the problem of learning sparse representations for tensor data by proposing a mixture of separable dictionaries model, and it develops algorithms and identifiability conditions, with numerical experiments demonstrating efficacy.
This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning. It proposes learning a mixture of separable dictionaries to better capture the structure of tensor data by generalizing the separable dictionary learning model. Two different approaches for learning mixture of separable dictionaries are explored and sufficient conditions for local identifiability of the underlying dictionary are derived in each case. Moreover, computational algorithms are developed to solve the problem of learning mixture of separable dictionaries in both batch and online settings. Numerical experiments are used to show the usefulness of the proposed model and the efficacy of the developed algorithms.