A multidimensional principal component analysis via the c-product Golub-Kahan-SVD for classification and face recognition

TL;DR

This paper introduces a tensor-based principal component analysis method using the Golub-Kahan-SVD via the tensor cosine product, enhancing face recognition and classification by efficiently extracting main features from tensor data.

Contribution

It proposes a novel tensor PCA approach based on the Golub-Kahan algorithm and tensor cosine product, addressing computational challenges in high-dimensional tensor data.

Findings

Effective face recognition demonstrated on numerical tests.

Tensor Golub-Kahan PCA outperforms traditional methods in accuracy.

Reduces computational complexity for large tensor datasets.

Abstract

Face recognition and identification is a very important application in machine learning. Due to the increasing amount of available data, traditional approaches based on matricization and matrix PCA methods can be difficult to implement. Moreover, the tensorial approaches are a natural choice, due to the mere structure of the databases, for example in the case of color images. Nevertheless, even though various authors proposed factorization strategies for tensors, the size of the considered tensors can pose some serious issues. When only a few features are needed to construct the projection space, there is no need to compute a SVD on the whole data. Two versions of the tensor Golub-Kahan algorithm are considered in this manuscript, as an alternative to the classical use of the tensor SVD which is based on truncated strategies. In this paper, we consider the Tensor Tubal Golub Kahan Principal Component Analysis method which purpose is to extract the main features of images using the tensor singular value decomposition (SVD) based on the tensor cosine product that uses the discrete cosine transform. This approach is applied for classification and face recognition and numerical tests show its effectiveness.