Efficient band approximation of Gram matrices for large scale kernel methods on GPUs
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Computers in Biology and Medicine
Distributed approximate spectral clustering for large-scale datasets
Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing
Real-time PCA calculation for spectral imaging (using SIMD and GP-GPU)
Journal of Real-Time Image Processing
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Kernel methods such as kernel principal component analysis and support vector machines have become powerful tools for pattern recognition and computer vision. Unfortunately the high computational cost of kernel methods is a limiting factor for real-time classification tasks when running on the CPU of a standard PC. Over the last few years, commodity Graphics Processing Units (GPU) have evolved from fixed graphics pipeline processors into more flexible and powerful data-parallel processors. These stream processors are capable of sustaining computation rates of greater than ten times that of a single CPU. GPUs are inexpensive and are becoming ubiquitous (desktops, laptops, PDAs, cell phones). In this paper, we present a face recognition system based on kernel methods running on the GPU. This GPU implementation is twenty eight times faster than the same optimized application running on the CPU.