kernel affine projection algorithms and kernel least-mean-square (KLMS) algorithmsliding window gram matrix inversionThe combination of the famed kernel trick and affine projection algorithms (APAs) yields powe
Kernel affine projection algorithms. EURASIP J. Adv. Signal Process. (2008) K. Slavakis et al. Sliding window generalized kernel affine projection algorithm using projection mappings EURASIP J. Adv. Signal Process. (2008)View more references ...
The best known KAFs, e.g., the kernel least mean-square (KLMS) algorithm [6], kernel affine projection algorithm (KAPA) [7], and kernel recursive least-square algorithm (KRLS) [8], outperform their corresponding linear counterparts in terms of filtering accuracy. Since KAFs need to ...
Gil-Cacho JM, Signoretto M, van Waterschoot T, Moonen M, Jensen SH (2013) Nonlinear acoustic echo cancellation based on a sliding-window leaky kernel affine projection algorithm. IEEE Trans Audio Speech Lang Process 21(9):1867–1878 Article Google Scholar Halimeh MM, Huemmer C, Kellermann ...
Leaky Kernel Affine Projection Algorithm (LKAPA, including KAPA-1 and KAPA-3) and Normalized Leaky Kernel Affine Projection Algorithm (NLKAPA, including KAPA-2 and KAPA-4), as proposed in W. Liu and J.C. Principe, "Kernel Affine Projection Algorithms", EURASIP Journal on Advances in Signal...
Circle coordinateFormatter Extent Geometry geometryEngine geometryEngineAsync HeightModelInfo Mesh Multipoint Point Polygon Polyline projection SpatialReference operators affineTransformOperator alphaShapeOperator areaOperator autoCompleteOperator boundaryOperator bufferOperator centroidOperator clipOperator containsOperator conv...
It learns an affine transformation via backpropagation, which is fur- ther applied to the feature map, trying to get an invariant expression of the feature. Such a global transformation is inefficient and difficult to learn, which cannot be applied to large scale datasets like ImageNet [3]. ...
GCAL内核另外还包含基本的操作,如仿射变换(affine transformation),相交的探测和计算及距离计算。 1.1Robustness The correctness proof of nearly all geometric algorithms presented in theory papers assumes exact computation with real numbers. This leads to a fundamental problem with the implementation of geometric...
This is simply because many machine learning algorithms (e.g., SVMs) can be entirely expressed in terms of dot products8, and, under some conditions, kernel functions can also be expressed as dot products in a (possibly infinite dimensional) feature space. Here, in order to both select ...
In recent years, the kernel least mean absolute third (KLMAT) algorithm (Lu et al., 2016), variable-mixing parameter quantized kernel robust mixed-norm algorithms (Lu et al., 2015), and kernel affine projection sign algorithm (Wang et al., 2013a, Wang et al., 2013b) have been develop...