Python implementation of the RLS-Nyström Method.

Below the original README.

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recursive-nystrom: Recursive Importance Sampling for the Nyström Method

MATLAB code implementing the recursive ridge leverage score sampling algorithm developed in: Recursive Sampling for the Nyström Method (NIPS 2017).

Installation

Download recursiveNystrom.m, add to MATLAB path, or include directly in project directory. For an example of usage see exampleApplication.m.

Usage

Input: recursiveNystrom(X,s,kernelFunc,accelerated_flag)

• X : matrix with n rows (data points) and d columns (features)
• s : number of samples used in Nyström approximation. default = sqrt(n). Generally should set s < n.
• kernelFunc : A function that can compute arbitrary submatrices of X's kernel matrix for some positive semidefinite kernel. For implementation specifics, see the provided example gaussianKernel.m. Default = gaussian kernel, i.e. e^{-γ||x - y||2}, with width parameter γ = 1.
• accelerated_flag: 0 or 1, default = 0. If the flag is set to 1, the code uses an accelerated version of the algorithm as described in Section 5.2.1 of the NIPS paper. This version will output a lower quality Nyström approximation, but run more quickly. We recommend setting accelerated_flag = 0 (the default) unless the standard version of the algorithm runs too slowly for your purposes.

Output: Rank s Nyström approximation, in factored form.

• C : A subset of s columns from X's n x n kernel matrix
• W : An s x s positive semidefinite matrix

C*W*C' approximates X's full kernel matrix, K.

In learning applications, it is natural to compute F = C*chol(W)'. F has n rows and s columns. Each row can be supplied as a data point to a linear algorithm (regression, SVM, etc.) to approximate the kernel version of the algorithm. Caveat: the accelerated version of our algorithm runs in O(ns) time. Computing F = C*chol(W)' takes O(ns²) time, so it may be more prudent to access the matrix implicitly.

Example

Compute a Nyström approximation for a Gaussian kernel matrix with variance parameter γ = 20

% generate random test matrix
X = randn(2000,100); X = normc(X);

% define function for computing kernel dot product
gamma = 20;
kFunc = @(X,rowInd,colInd) gaussianKernel(X,rowInd,colInd,gamma);

% compute factors of Nyström approximation
[C,W] = recursiveNystrom(X,500,kFunc);