Approximating non-linear kernels using fea-ture maps has gained a lot of interest in re-cent years due to applications in reducing training and testing times of SVM classifiers and other kernel based learning algorithms. We extend this line of work and present low distortion embeddings for dot product ker-nels into linear Euclidean spaces. We base our results on a classical result in harmonic analysis characterizing all dot product ker-nels and use it to define randomized feature maps into explicit low dimensional Euclidean spaces in which the native dot product pro-vides an approximation to the dot product kernel with high confidence.
A major paradigm for learning image representations in a self-supervised manner is to learn a model ...
Devoted to multi-task learning and structured output learning, operator-valued kernels provide a fle...
Random feature maps are a promising tool for large-scale kernel methods. Since most random feature m...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
Kernel approximation using randomized fea-ture maps has recently gained a lot of in-terest. In this ...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
Although kernel methods efficiently use feature combinations without computing them directly, they d...
Kernel approximation using random feature maps has recently gained a lot of interest. This is mainly...
With the goal of accelerating the training and test-ing complexity of nonlinear kernel methods, seve...
Dot product kernels, such as polynomial and exponential (softmax) kernels, are among the most widely...
We present a new framework for online Least Squares algorithms for nonlinear modeling in RKH spaces ...
Many interesting machine learning problems are best posed by considering instances that are distribu...
Kernel methods represent one of the most powerful tools in machine learning to tackle problems expre...
In order to grapple with the conundrum in the scalability of kernel-based learning algorithms, the m...
Approximations based on random Fourier features have recently emerged as an efficient and formally c...
A major paradigm for learning image representations in a self-supervised manner is to learn a model ...
Devoted to multi-task learning and structured output learning, operator-valued kernels provide a fle...
Random feature maps are a promising tool for large-scale kernel methods. Since most random feature m...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
Kernel approximation using randomized fea-ture maps has recently gained a lot of in-terest. In this ...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
Although kernel methods efficiently use feature combinations without computing them directly, they d...
Kernel approximation using random feature maps has recently gained a lot of interest. This is mainly...
With the goal of accelerating the training and test-ing complexity of nonlinear kernel methods, seve...
Dot product kernels, such as polynomial and exponential (softmax) kernels, are among the most widely...
We present a new framework for online Least Squares algorithms for nonlinear modeling in RKH spaces ...
Many interesting machine learning problems are best posed by considering instances that are distribu...
Kernel methods represent one of the most powerful tools in machine learning to tackle problems expre...
In order to grapple with the conundrum in the scalability of kernel-based learning algorithms, the m...
Approximations based on random Fourier features have recently emerged as an efficient and formally c...
A major paradigm for learning image representations in a self-supervised manner is to learn a model ...
Devoted to multi-task learning and structured output learning, operator-valued kernels provide a fle...
Random feature maps are a promising tool for large-scale kernel methods. Since most random feature m...
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