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» On the granularity of summative kernels
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ICPR
2008
IEEE
13 years 11 months ago
Non-linear feature extraction by linear PCA using local kernel
This paper presents how to extract non-linear features by linear PCA. KPCA is effective but the computational cost is the drawback. To realize both non-linearity and low computati...
Kazuhiro Hotta
ICASSP
2010
IEEE
13 years 5 months ago
Nonlinear kernel backprojection for computed tomography
In this paper, we propose a kernel backprojection method for computed tomography. The classical backprojection method estimates an unknown pixel value by the summation of the proj...
Hiroyuki Takeda, Peyman Milanfar
FCCM
2011
IEEE
241views VLSI» more  FCCM 2011»
12 years 9 months ago
Multilevel Granularity Parallelism Synthesis on FPGAs
— Recent progress in High-Level Synthesis (HLS) es has helped raise the abstraction level of FPGA programming. However implementation and performance evaluation of the HLS-genera...
Alexandros Papakonstantinou, Yun Liang, John A. St...
NIPS
2001
13 years 6 months ago
Stochastic Mixed-Signal VLSI Architecture for High-Dimensional Kernel Machines
A mixed-signal paradigm is presented for high-resolution parallel innerproduct computation in very high dimensions, suitable for efficient implementation of kernels in image proce...
Roman Genov, Gert Cauwenberghs
VDA
2010
185views Visualization» more  VDA 2010»
13 years 7 months ago
Visualizing multidimensional data through granularity-dependent spatialization
Spatialization is a special kind of visualization that projects multidimensional data into low-dimensional representational spaces by making use of spatial metaphors. Spatializati...
Sofia Kontaxaki, Eleni Tomai, Margarita Kokla, Mar...