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Joint Gaussian Process Regression (JGPR)

This is the implementation of this paper for multi-target regression problems, named Joint Gaussian Process Regression (JGPR).

R Packages

We have tested the JGPR with R 4.1.2 and the following packages:

package version
Deriv 4.1.3
rdist 0.0.5
progress 1.2.2
R.utils 2.11.0

Geting started with JGPR

The JGPR takes the following parameters and returns a trained model.

  • inputs: training inputs
  • outputs: training outputs (targets)
  • kern: user-defined kernel
  • init.params: initializing of kernel parameters
  • fix.noise: If true the noise kernel is not optimized in the training phase
  • inp: preprocessing of inputs (it can be 'st' or 'norm', which show standardization and normalization process, respectively)
  • out: preprocessing of outputs (it can be 'st' or 'norm', which show standardization and normalization process, respectively)
  • MaxIter: maximum Iteration for training
  • break.tol: if true Iteration stops with special condition
  • e: multiply parameter to Back tracking line search
  • init.rate: initialization rate of optimization

Then, the trained model can predict the targets of newly arrived test samples. The predict function takes the following parameters:

  • newInputs: new inputs for predicting the targets
  • sigma.opt: Noise kernel parameter (this parameter is optimized in the training process by default. However, it can be set manully here)

Example

to load the JGPR function from your current directory you can use the following code:

script.dir <- dirname(sys.frame(1)$ofile) #load current script directory
source(sprintf('%s/JGPR.R', script.dir)) #load JGPR from the current directory

The following code is a part of example.R file.

#preparing data
x.tr = as.matrix(seq(0, 10, 0.5), ncol = 1) #training inputs
x.ts = as.matrix(seq(0, 10, 0.01), ncol = 1) #test inputs

y.tr = matrix(0, nrow = length(x.tr), ncol = 8) #real values of training outputs
y.trn = matrix(0, nrow = length(x.tr), ncol = 8) #training outputs + noise
y.ts = matrix(0, nrow = length(x.ts), ncol = 8) #test outputs

noiseDev = 0.8
s = 11111

for (i in 1:8) {
  set.seed((20+i-1)*s)
  
  y.tr[, i] = sin(x.tr + 0.2*(i-1))
  y.trn[, i] = sin(x.tr + 0.2*(i-1)) + rnorm(length(x.tr), 0, noiseDev)
  y.ts[, i] = sin(x.ts + 0.2*(i-1))

}

#-------------------------------------------------------------------------------
#define kernel function
kern = quote(v1^2*exp(-d^2/v2^2))

#-------------------------------------------------------------------------------
#JGPR
model = JGPR(x.tr, y.trn, kern = kern, init.params = c(0.1, 1, 0.1), MaxIter = 100)
pred = model$predict(x.ts)

The following figure shows the result of using JGPR and conventional GPR in the toy multi-target regression problem. To get this figure please run the example.R file. result Please note that JGPR acts the same as conventional GPR (CGPR) for a one-dimensional target.

Kernel function

The JGPR can be run with a user-defined kernel function. We can use v1, v2, ..., vm coefficients inside the quote function for defining a kernel. These coefficients are optimized in the training phase. Also, d and ip indicate the euclidean distance and inner product, respectively. We have provided some examples of kernels with their respected code.

kernel code
$k(x_i, x_j) = v_1^2 exp({ - \dfrac{d^2(x_i , x_j)}{v_2^2}) }$ quote(v1^2*exp(-d^2/v2^2))
$k(x_i, x_j) = v_1^2 x_i x_j$ quote(v1^2*ip)
$k(x_i, x_j) = v_1^2 exp\left(- \dfrac{ sin^2(d(x_i, x_j) / v_2^2) }{v_3^2} \right)$ quote(v1^2*exp(-(sin(d/v2^2))^2/v3^2))

The v1, v2, ..., vm variables are optimized with initialization values, which is set in the init.params in the JGPR function. The init.params takes m+1 values, which the last value is initialization value of the noise kernel. For instance, if we have v1 and v2 for defination of the kernel, the init.params takes three parameters (e.g., c(1, 1, 0.05)).

Citation

Please cite JGPR in your publications if it helps your research. The following references are the BibTeX format of our paper and related letter to the JGPR.

@ARTICLE{Nabati2022,
  author={Nabati, Mohammad and Ghorashi, Seyed Ali and Shahbazian, Reza},
  journal={Machine Learning}, 
  title={JGPR: a computationally efficient multi-target Gaussian process regression algorithm}, 
  year={2022}
  volume={111},
  number={6},
  pages={1987–2010},
  doi={https://doi.org/10.1007/s10994-022-06170-3}}
@ARTICLE{Nabati2021,
  author={Nabati, Mohammad and Ghorashi, Seyed Ali and Shahbazian, Reza},
  journal={IEEE Communications Letters}, 
  title={Joint Coordinate Optimization in Fingerprint-Based Indoor Positioning}, 
  year={2021},
  volume={25},
  number={4},
  pages={1192-1195},
  doi={10.1109/LCOMM.2020.3047352}}