|book-title=Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR’05)
|___location=San Diego, CA
|year=2005}}</ref> introduced algorithmic clarity. They synthesized a set of ideas into an elegant two-step algorithm—one whose simplicity belies the complexity it resolves. Vasilescu and Terzopoulos introduced the '''M-mode SVD''' which is currently referred in the literature as the '''Tucker''' or the '''HOSVD'''. However, the Tucker algorithm, and De Lathauwer ''et al.'''s companion algorithm<ref name=":2"/> are sequential, relying on iterative methods such as gradient descent or the power method, respectively. Vasilescu and Terzopoulos synthesized a set of ideas into an elegant two-step algorithm that can be executed sequentially or in parallel, whose simplicity belies the complexity it resolves. The term '''M-mode SVD''' accurately reflects the algorithm employed without overpromising; it captures the actual computation (a set of SVDs on mode-flattenings) without making assumptions about the structure of the core tensor or implying a rank decomposition. It can be computed sequentially, but is also well-suited for parallel computation.
: This misattribution has had lasting impact on the scholarly record, obscuring the original source of a widely adopted algorithm, and complicating efforts to trace its development, reproduce results, and recognizing the respective contributions of different research efforts.
