Séminaire de l’équipe MSDMA, vendredi 23 Février 2024

Orthonormality is the foundation of a number of matrix decomposition methods. For example, Singular Value Decomposition (SVD) implements the compression by factoring a matrix with orthonormal parts and is pervasively utilized in various fields. Orthonormality, however, inherently includes constraints that would induce redundant information,  and make the manipulation of orthonormal matrix difficult. An enhanced version of SVD, namely E-SVD, is accordingly established to losslessly and quickly release constraints and recover the orthonormal parts in SVD. E-SVD has a wide range of application field. For data compression, E-SVD will reduce 25% storage units as SVD reaches its limitation and fails to compress data. For blind watermarking, E-SVD theoretically guarantees the full retrievability of the watermark in the absence of an attack. For object detection, E-SVD give a new perspective of figuring out spatial variation in a matrix, leading to a wider usage of matrix factorization methods in the domain of unsupervised object detection.