  The early detection of breast abnormalities is a matter of critical
significance. Notably, infrared thermography has emerged as a valuable tool in
breast cancer screening and clinical breast examination (CBE). Measuring
heterogeneous thermal patterns is the key to incorporating computational
dynamic thermography, which can be achieved by matrix factorization techniques.
These approaches focus on extracting the predominant thermal patterns from the
entire thermal sequence. Yet, the task of singling out the dominant image that
effectively represents the prevailing temporal changes remains a challenging
pursuit within the field of computational thermography. In this context, we
propose applying James-Stein for eigenvector (JSE) and Weibull embedding
approaches, as two novel strategies in response to this challenge. The primary
objective is to create a low-dimensional (LD) representation of the thermal
data stream. This LD approximation serves as the foundation for extracting
thermomics and training a classification model with optimized hyperparameters,
for early breast cancer detection. Furthermore, we conduct a comparative
analysis of various embedding adjuncts to matrix factorization methods. The
results of the proposed method indicate an enhancement in the projection of the
predominant basis vector, yielding classification accuracy of 81.7% (+/-5.2%)
using Weibull embedding, which outperformed other embedding approaches we
proposed previously. In comparison analysis, Sparse PCT and Deep SemiNMF showed
the highest accuracies having 80.9% and 78.6%, respectively. These findings
suggest that JSE and Weibull embedding techniques substantially help preserve
crucial thermal patterns as a biomarker leading to improved CBE and enabling
the very early detection of breast cancer.
