Statistical decomposition methods are of paramount im-
portance in discovering the modes of variations of visual
data. Probably the most prominent linear decomposition
method is the Principal Component Analysis (PCA), which
discovers a single mode of variation in the data. However,
in practice, visual data exhibit several modes of variations.
For instance, the appearance of faces varies in identity, ex-
pression, pose etc. To extract these modes of variations from
visual data, several supervised methods, such as the Ten-
sorFaces, that rely on multilinear (tensor) decomposition
(e.g., Higher Order SVD) have been developed. The main
drawbacks of such methods is that they require both labels
regarding the modes of variations and the same number of
samples under all modes of variations (e.g., the same face
under different expressions, poses etc.). Therefore, their ap-
plicability is limited to well-organised data, usually cap-
tured in well-controlled conditions. In this paper, we pro-
pose the first general multilinear method, to the best of our
knowledge, that discovers the multilinear structure of visual
data in unsupervised setting. That is, without the presence
of labels. We demonstrate the applicability of the proposed
method in two applications, namely Shape from Shading
(SfS) and expression transfer.