… model the Multilinear ICA (MICA) decomposition of the distribution P(X). Of all possible
multilinear … In [1] we successfully deployed the new method to model natural scene textures and …

… fast PCA algorithm embedding an incremental multilinear method. Based on the characteristics
of … accuracy such as LDA, ICA, Kernal PCA and other PCA related algorithms [33-35]. For …

… refinement of ICA the Multilinear ICA (MICA) Model. We successfully deploy the new method
to model … relative to classical ICA. We conclude in Section IV with a discussion of possible …

… An ICA can be performed by resorting to multilinear SVD-extensions. We establish links with
… In this thesis the ICA-problem is tackled with multilinear algebraic techniques; for a literature …

… and generalization of the proposed SOH prediction method are verified under the evaluation
of … using ICA and EMD to develop an integrated Li-ion battery SOH prediction method with …

… that, when dealing with an unfolding of dimensionality Ic × IaIb, we formally assume that the
… We observe that the algorithm leads to unsymmetric intermediate results, not located on the …

… -order statistics in the context of ICA [9, 14]. The low multilinear rank approximation that is
considered in this paper can then be used as a dimensionality reduction tool for ICA [19]. As …

… new multilinear PCA (MPCA) algorithm is proposed. It is a natural extension of PCA to the
multilinear … on gait tensor samples, we need to normalize the samples to a canonical size of Ic …

… In particular, we will discuss multilinear generalizations of PCA and ICA and present new …
The N-mode SVD algorithm for decomposing D according to (1) is a multilinear extension of …

… To show the validity of the method, we apply it to a plant leaf image dataset. The … , we will
study the multilinear generalization of ICA. Dimensionality reduction by ICA is usually based on …