Principal Component Analysis: A Generalized Gini Approach

Our paper, with Stéphane Mussard and Téa Ouraga, entitle Principal Component Analysis: A Generalized Gini Approach is finally out in the European Journal of Operations Research.

A principal component analysis based on the generalized Gini correlation index is proposed (Gini PCA). The Gini PCA generalizes the standard PCA based on the variance. It is shown, in the Gaussian case, that the standard PCA is equivalent to the Gini PCA. It is also proven that the dimensionality reduction based on the generalized Gini correlation matrix, that relies on city-block distances, is robust to outliers. Monte Carlo simulations and an application on cars data (with outliers) show the robustness of the Gini PCA and provide different interpretations of the results compared with the variance PCA.

Some general thoughts on Partial Dependence Plots with correlated covariates

The partial dependence plot is a nice tool to analyse the impact of some explanatory variables when using nonlinear models, such as a random forest, or some gradient boosting.The idea (in dimension 2), given a model m(x_1,x_2) for \mathbb{E}[Y|X_1=x_1,X_2=x_2]. The partial dependence plot for variable x_1 is model m is function p_1 defined as x_1\mapsto\mathbb{E}_{\mathbb{P}_{X_2}}[m(x_1,X_2)]. This can be approximated, using some dataset using \widehat{p}_1(x_1)=\frac{1}{n}\sum_{i=1}^n m(x_1,x_{2,i})My concern here what the interpretation of that plot when there are some (strongly) correlated covariates. Let us generate some dataset to start with

X = rmnorm(n,mean = c(0,0),varcov = matrix(c(1,r,r,1),2,2))
Y = 1+X[,1]-2*X[,2]+rnorm(n)/2
df = data.frame(Y=Y,X1=X[,1],X2=X[,2])

As we can see, the true model is here is y_i=\beta_0+\beta_1 x_{1,i}+\beta_2x_{2,i}+\varepsilon_i where \beta_1 =1 but the two variables are positively correlated, and the second one has a strong negative impact. Note that here

reg = lm(Y~.,data=df)
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.01414    0.01601   63.35   <2e-16 ***
X1           1.02268    0.02305   44.37   <2e-16 ***
X2          -2.03248    0.02342  -86.80   <2e-16 ***

If we estimate a wrongly specified model y_i=b_0+b_1 x_{1,i}+\eta_i, we would get

reg1 = lm(Y~X1,data=df)
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.03522    0.04680  22.121   <2e-16 ***
X1          -0.44148    0.04591  -9.616   <2e-16 ***

Thus, on the proper model, \widehat{\beta}_1\sim+1.02 while \widehat{b}_1\sim-0.44  on the mispecified model.

Now, let us look at the parial dependence plot of the good model, using standard R dedicated packages,

pdp::partial(reg, pred.var = "X1", plot = TRUE,
              plot.engine = "ggplot2")

which is the linear line y=1+x, that corresponds to y=\beta_0+\beta_1x.

data=df),variable = "X1",type = "pdp"))

which corresponds to the previous graph. Here, it is also possible to creaste our own function to compute that partial dependence plot,

pdp1 = function(x1){
  nd = data.frame(X1=x1,X2=df$X2)

that will be the straight line below (the dotted line is the theoretical one y=1+x,

vpdp1 = Vectorize(pdp1)(vx)

which is very different from the univariate regression on x_1


Actually, the later is very consistent with a local regression, only on x_1


Now, to get back to the definition of the partial dependence plot, x_1\mapsto\mathbb{E}_{\mathbb{P}_{X_2}}[m(x_1,X_2)], in the context of correlated variable, I was wondering if it would not make more sense to consider some local version actually, something like x_1\mapsto\mathbb{E}_{\mathbb{P}_{X_2|X_1}}[m(x_1,X_2)]. My intuition was that, somehow, it did not make any sense to consider any X_2 while X_1 was fixed (and equal to x_1). But it would make more sense actually to look at more valid X_2‘s given the value of X_1. And a natural estimate could be some k neareast-neighbors, i.e. \tilde{p}_1(x_1)=\frac{1}{k}\sum_{i\in\mathcal{V}_k(x)}^n m(x_1,x_{2,i}) where \mathcal{V}_k(x) is the set of indices of the k x_i‘s that are the closest to x, i.e.

lpdp1 = function(x1){
  nd = data.frame(X1=x1,X2=df$X2)
  idx = rank(abs(df$X1-x1))
vlpdp1 = Vectorize(lpdp1)(vx)

Surprisingly (?), this local partial dependence plot gives a curve that corresponds to the simple regression…