Intellectual evolution of social innovation: a bibliometric analysis and avenues for future research trends

Despite the fact that the concept of social innovation is extensively employed by scholars and practitioners, yet the conceptualisation and the research structure remained fragmented and scattered, because no rigorous attempt has been made to understand the core concept of social innovation. The notion of social innovation is multi-faceted and multi-disciplinary fluctuating from public-policy to environmental sustainability; which makes an investigation of the concept essential for business-to-business practitioners and scholars. By processing 370 publications from a sample of 125 journals and books with a total of 2941 citations, the authors unpack/unfold the intellectual foundation of social innovation in business and management domains by performing four bibliometric analyses and they evaluate the research domain qualitatively (1970-2019). By using co-citation, network visualisation through co-occurrence data, multi-dimensional scaling, and hierarchical cluster analysis, this research sheds light to the intellectual structure of social innovation including social value, economic value, societal impact, and bifocal innovations. This research reveals the key research clusters embodied by social innovation foundation. The present study identifies four important components for the future avenues of social innovation (i.e. opportunity, innovation practice, opportunity exploiter, value), and proposes a potential research framework to the researchers and practitioners, hoping to provide insights on social innovation

Grassmann Learning for Recognition and Classification

Computational performance associated with high-dimensional data is a common challenge for real-world classification and recognition systems. Subspace learning has received considerable attention as a means of finding an efficient low-dimensional representation that leads to better classification and efficient processing. A Grassmann manifold is a space that promotes smooth surfaces, where points represent subspaces and the relationship between points is defined by a mapping of an orthogonal matrix. Grassmann learning involves embedding high dimensional subspaces and kernelizing the embedding onto a projection space where distance computations can be effectively performed. In this dissertation, Grassmann learning and its benefits towards action classification and face recognition in terms of accuracy and performance are investigated and evaluated. Grassmannian Sparse Representation (GSR) and Grassmannian Spectral Regression (GRASP) are proposed as Grassmann inspired subspace learning algorithms. GSR is a novel subspace learning algorithm that combines the benefits of Grassmann manifolds with sparse representations using least squares loss §¤1-norm minimization for improved classification. GRASP is a novel subspace learning algorithm that leverages the benefits of Grassmann manifolds and Spectral Regression in a framework that supports high discrimination between classes and achieves computational benefits by using manifold modeling and avoiding eigen-decomposition. The effectiveness of GSR and GRASP is demonstrated for computationally intensive classification problems: (a) multi-view action classification using the IXMAS Multi-View dataset, the i3DPost Multi-View dataset, and the WVU Multi-View dataset, (b) 3D action classification using the MSRAction3D dataset and MSRGesture3D dataset, and (c) face recognition using the ATT Face Database, Labeled Faces in the Wild (LFW), and the Extended Yale Face Database B (YALE). Additional contributions include the definition of Motion History Surfaces (MHS) and Motion Depth Surfaces (MDS) as descriptors suitable for activity representations in video sequences and 3D depth sequences. An in-depth analysis of Grassmann metrics is applied on high dimensional data with different levels of noise and data distributions which reveals that standardized Grassmann kernels are favorable over geodesic metrics on a Grassmann manifold. Finally, an extensive performance analysis is made that supports Grassmann subspace learning as an effective approach for classification and recognition