The Rapid Prototyping of Application Specific signal Processors (RASSP) program is a multi-year DARPA/Tri-Service initiative intended to dramatically improve the process by which complex digital systems, particularly ...
The Rapid Prototyping of Application Specific signal Processors (RASSP) program is a multi-year DARPA/Tri-Service initiative intended to dramatically improve the process by which complex digital systems, particularly embedded digital signal processors, are designed, manufactured, upgraded, and supported. This paper reviews the genesis of the RASSP program, considering both the problems that defined the need for the program, and the historical conditions under which it began. The RASSP program is then presented from two viewpoints. The first is programmatic, covering the goals and constraints of the program, and describing the roles of the various program participants. The second is technical, covering the major concepts upon which the developing RASSP approach to design is based and showing how the detailed technical discussions contained in the other papers in this issue of the Journal of VLSI signalprocessing relate to one another and fit into an overall development concept. The paper closes with a review of the status of the program as of this writing (Summer 1996).
The Anchor-based Multi-view Subspace Clustering (AMSC) has turned into a favourable tool for large-scale multi-view clustering. However, there still exist some limitations to the current AMSC approaches. First, they t...
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The Anchor-based Multi-view Subspace Clustering (AMSC) has turned into a favourable tool for large-scale multi-view clustering. However, there still exist some limitations to the current AMSC approaches. First, they typically recover anchor graph structure in the original linear space, restricting their feasibility for nonlinear scenarios. Second, they usually overlook the potential benefits of jointly capturing the inter-view and intra-view information for enhancing the anchor representation learning. Third, these approaches mostly perform anchor-based subspace learning by a specific matrix norm, neglecting the latent high-order correlation across different views. To overcome these limitations, this paper presents an efficient and effective approach termed Large-scale Tensorized Multi-view Kernel Subspace Clustering (LTKMSC). Different from the existing AMSC approaches, our LTKMSC approach exploits both inter-view and intra-view awareness for anchor-based representation building. Concretely, the low-rank tensor learning is leveraged to capture the high-order correlation (i.e., the inter-view complementary information) among distinct views, upon which the \(l_{1,2}\) norm is imposed to explore the intra-view anchor graph structure in each view. Moreover, the kernel learning technique is leveraged to explore the nonlinear anchor-sample relationships embedded in multiple views. With the unified objective function formulated, an efficient optimization algorithm that enjoys low computational complexity is further designed. Extensive experiments on a variety of multi-view datasets have confirmed the efficiency and effectiveness of our approach when compared with the other competitive approaches.
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