## 3 Related Research  
A multi-view study, as cited in [4], establishes the similarities between two different views. There are several other published methods used to serve the same purpose. These methods have been published with the intention of concentrating their interest on expressive as well as discriminative facets from low-level observations [9; 10; 11; 12; 13; 14]. Similarly, [15] entirely used the intrinsic characteristics extracted from the views by combining color and depth information, which resulted in the improvement of their perspective-invariant feature transform (PIFT) for RGB-D images. However, [15] focused on the combined used of the RGB and depth component and gave less attention to the features (shared and global) to be extracted from each viewpoint. [PERSON] et al. [16] obtained a dictionary that can be used to convert 2D video to a view-invariant sparse representation, as well as a classifier to recognize actions with an arbitrary view by using an end-to-end framework to learn view invariance jointly. The authors of [16] also introduced a 3D trajectory which can describe the action better; however, it does not emphasize the percentage of contribution of each view to that trajectory. [PERSON] et al. [17] took a temporal sequence of graphs as an illustration of a graph representation action and used that to create a feature descriptor by applying a spectral graph wavelet transform. The authors of [17] also emphasized two well-known types of view-invariant graphs--key point-based and skeleton-based graphs. [PERSON] et al. [18] devised a histogram of oriented principal components (HOPC) that is robust to noise. Unlike several articles, [18] obtained cloud points by directly dealing with point clouds for cross-view action recognition from unknown and unseen views. Also, [19] proposed a different approach when extracted functionality is needed. [PERSON] et al. [19] used the Euclidean distance between the spatiotemporal characteristic vectors represented in a spatiotemporal matrix (STM). [PERSON] and [PERSON] [20] presented a cotraining technique that trains various learning algorithms for every view and determines a certain correlation between two pairs of information among various views. [PERSON] et al. [21] and [PERSON] et al. [22] introduced another approach called canonical correlation analysis  
Figure 2: The figure shows a recap of our features extractor. (CCA) that helps in monitoring a common space among various views. Moreover, in [23], a challenge was encountered when studying an incomplete view owing to an assumption that multiple views are created from a shared space. [PERSON] et al. [24] provided another approach called generalized multi-view analysis (GMA). According to [PERSON] et al. [11], which found that matrix factorization was applicable in the clustering of multiple views, [10] was introduced alongside the collective matrix factorization (CMF) method, which learns relationships between feature matrices. Similarly, [PERSON] et al. [14] came up with a low-rank controlled matrix factorization model that provides a solution to the challenge faced in the multi-view learning. Various studies have tried to provide solutions to the challenges associated with view-invariant action recognition. One of these challenges is related to the generation of action labels in a scenario where multiple views are involved. [PERSON] et al. [25] introduced an approach that offers a structured categorization of the 3D histogram of oriented gradients (HOG) and local separation with an intention to represent successive images. [PERSON] and colleagues [5; 26] showed an SSM-based approach that extracts view-invariant descriptors in a log-polar block of the matrix by determining a frame-wise resemblance matrix in a video. However, a multitask learning technique can be used to enhance the SSM power of the representation [7]. This technique comprises shared facets among different views as examined in [6; 8; 27; 28], where more explicit approaches exist. [PERSON] et al.