High-dimensional problem poses significant challenges for dictionary learning based
classification architecture. Joint Dimension Reduction and Dictionary Learning (JDRDL) …

Conventional feature extraction methods generally focus on extracting global and local
features from the original data or converting a high dimensional space to a lower …

Image set classification, which compares the similarity between image sets with variable
quantity, quality and unordered heterogeneous images, has drawn increased research …

The increasing use of multiple sensors, which produce a large amount of multi-dimensional
data, requires efficient representation and classification methods. In this paper, we present a …

Representing image sets as subspaces on Grassmann manifold and leveraging the
Riemannian geometry of this space has proven to be highly effective in various visual …

Set based image classification technology has been developed successfully in recent
decades. Previous approaches dispose set based image classification by employing all the …

In image set classification, affine hull based models have achieved good performance.
However, these models only focus on holistic images and ignore the local information of …

This work proposes an interpretable multi-view deep neural network architecture, namely
optimal discriminant multi-view tensor convolutional network (ODMTCNet), by integrating …

Pattern-set matching belongs to a class of problems where learning takes place through sets
rather than elements. Much used in computer vision, this approach has the advantage of …

An optimized neural network classification method based on kernel holistic learning and
division (KHLD) is presented. The proposed method is based on the learned radial basis …