The gradfps R package implements the gradient-based Fantope projection and selection
algorithm, a convex formulation of sparse principal component analysis. The algorithm
is based on the paper
Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning
by Yixuan Qiu, Jing Lei, and Kathryn Roeder.
Currently gradfps has not been submitted to CRAN, but it can be installed just like
any other R packages hosted on GitHub. For devtools users, the following command
should work on most platforms:
library(devtools)
install_github("yixuan/gradfps")Note that a C++ compiler that supports the C++11 standard is needed. For best performance, it is strongly suggested linking your R to the OpenBLAS library for matrix computation. You can achieve this with the help of the ropenblas package.
Here we compare our implementation with the original ADMM-based algorithm (via the package fps developed by Vincent Vu).
Note that for a fair comparison, I added a new function fps_benchmark() to the
original fps package, and the example below requires the installation of my
modified version:
devtools::install_github("yixuan/fps")And then run the example code:
library(gradfps)
library(fps) # The modified version
library(RSpectra)
library(mvtnorm)
library(Matrix)
library(ggplot2)
n = 200
p = 800
d1 = 20 # first group
d2 = 15 # second group
# Generate covariance matrices
# Simulate eigenvectors
set.seed(123)
ev = matrix(rnorm(p^2), p, p)
ev[, 1:2] = 0
ev[1:d1, 1] = runif(d1, 0.9 / sqrt(d1), 1.1 / sqrt(d1))
ev[(d1 + 1):(d1 + d2), 2] = runif(d2, 0.9 / sqrt(d2), 1.1 / sqrt(d2))
ev = qr.Q(qr(ev))
# Simulate eigenvalues
sigmas = c(12, 6, runif(p - 2, 0, 2))
# True covariance
Sigma = ev %*% diag(sigmas) %*% t(ev)
# Visualization of the true covariance matrix
view_matrix(Sigma[1:100, 1:100], legend_title = "True\nCovariance\nMatrix")
# Eigenvectors
d = 5
V = eigs_sym(Sigma, d)$vectors
Pi = tcrossprod(V[, 1:2])
# Visualization of the true eigenvectors
view_evec(-V[1:200, ], asp = 0.4, bar_height = 8)
# Generate data
set.seed(123)
z = rmvnorm(n, sigma = Sigma)
Smat = crossprod(z) / n
d = 2
lambda = 0.5 * sqrt(log(p) / n)
e = eigs_sym(Smat, d, which = "LA")
# Initial value, should be noisy
x0 = tcrossprod(e$vectors)
# ADMM FPS
res_fps = fps::fps_benchmark(
Smat, d, lambda, x0, Pi, rho = -1, maxiter = 60, tolerance = 1e-3, verbose = 0
)
# Verify the result
view_matrix(res_fps$projection[1:100, 1:100], legend_title = "ADMM-FPS\nProjection\nMatrix")
# Gradient FPS
res_grad = gradfps_prox_benchmark(
Smat, Pi, d, lambda, x0, lr = 0.02, maxiter = 60,
control = list(fan_maxinc = 10, verbose = 0)
)
# Verify the result
view_matrix(res_grad$projection[1:100, 1:100], legend_title = "GradFPS\nProjection\nMatrix")
# Compare computational efficiency
gdat = data.frame(
method = c(rep("ADMM-FPS", length(res_fps$times)), rep("GradFPS", length(res_grad$times))),
time = c(cumsum(res_fps$times), cumsum(res_grad$times)),
err = c(res_fps$errors, res_grad$err_v),
par = sprintf("n = %d, p = %d", n, p)
)
ggplot(gdat, aes(x = time, y = err)) +
geom_line(aes(group = method, color = method, linetype = method), size = 1) +
scale_linetype_manual("Method", values = c("32", "solid")) +
scale_color_hue("Method") +
guides(color = guide_legend(keyheight = 2, keywidth = 3),
linetype = guide_legend(keyheight = 2, keywidth = 3)) +
xlab("Elapsed Time (s)") + ylab("Estimation Error") +
ggtitle(sprintf("n = %d, p = %d", n, p)) +
theme_bw(base_size = 20) +
theme(plot.title = element_text(hjust = 0.5))
Please consider to cite our work if you have used our algorithm or
package in your research. The code to reproduce the experiments in
the article can be found in the inst/article directory.
@article{qiu2022gradient,
title={Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning},
author={Qiu, Yixuan and Lei, Jing and Roeder, Kathryn},
journal={Biometrika},
year={2022},
month={07},
issn = {1464-3510},
doi = {10.1093/biomet/asac041}
}
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