Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation

In a world increasingly driven by data, the realms of nonnegative matrix and tensor factorizations reveal their significance in extracting meaningful patterns from complex datasets. This work combines theory and application, showcasing how these methodologies can be utilized for exploration in multi-way data analysis and blind source separation.

Readers are introduced to a wide range of models that facilitate the decomposition of nonnegative data, paving the way for enhanced understanding and interpretation. The authors engage with various efficient algorithms that make these powerful techniques accessible, regardless of the reader's prior knowledge in the field.

Delving into practical applications, this volume illustrates how such factorization techniques can be harnessed in real-world scenarios, making it an essential resource for researchers and practitioners alike. The combination of foundational concepts with innovative methodologies provides a comprehensive overview of the advancements in data analysis.