# Clearing the Confusion: PCA and UMAP

Principal Components Analysis (PCA) is a well-established method of dimension reduction. It is often used as a means of gaining insight into the “hidden meanings” in a dataset. But in prediction contexts–ML–it is mainly a technique for avoiding overfitting and excess computation.

This tutorial will give a more concrete, more real-world-oriented, overview of PCA than those given in most treatments.

A number of “nonlinear versons” of PCA have been developed, including Uniform Manifold Approximation and Projection (UMAP), which we will also discuss briefly here.

# Needed background

Facility in matrix algebra is needed to fully understand the treatment here, but those without such background should be able to follow most of it. The contents of the Overfitting vignette are also helpful here.

# PCA

All PCA does is form new columns from the original columns of our data. Those new columns form our new feature set. It’s that simple.

Each new column is some linear combination of our original columns. Moreover, the new columns are uncorrelated with each other, the importance of which we will also discuss.

Let’s see concretely what all that really means.

## Example: mlb data

Consider mlb, a dataset included with qeML. We will look at heights, weights and ages of American professional baseball players. (We will delete the first column, which records position played.)

> data(mlb1)
> mlb <- mlb1[,-1]
> head(mlb)
Height Weight   Age
1     74    180 22.99
2     74    215 34.69
3     72    210 30.78
4     72    210 35.43
5     73    188 35.71
6     69    176 29.39
> dim(mlb)
[1] 1015    3
# 1015 players, 3 measurements each
# PCA requires matrix format
> mlb <- as.matrix(mlb)  

## Apply PCA

The standard PCA function in R is prcomp:

# defaults center=TRUE, scale.=FALSE)
> z <- prcomp(mlb)  

We will look at the contents of z shortly. But first, it is key to note that all that is happening is that we started with 3 variables, Height, Weight and Age, and now have created 3 new variables, PC1, PC2 and PC3. Those new variables are stored in the matrix z$x. Again, we will see the details below, but the salient point is: 3 new features. We originally had 3 measurements on each of 1015 people, and now we have 3 new measurements on each of those people: > dim(z$x)
[1] 1015    3

Well then, what is in there in z?

> str(z)
List of 5
$sdev : num [1:3] 20.87 4.29 1.91$ rotation: num [1:3, 1:3] -0.0593 -0.9978 -0.0308 -0.1101 -0.0241 ...
..- attr(*, "dimnames")=List of 2
.. ..$: chr [1:3] "Height" "Weight" "Age" .. ..$ : chr [1:3] "PC1" "PC2" "PC3"
$center : Named num [1:3] 73.7 201.3 28.7 ..- attr(*, "names")= chr [1:3] "Height" "Weight" "Age"$ scale   : logi FALSE
$x : num [1:1015, 1:3] 21.46 -13.82 -8.6 -8.74 13.14 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:1015] "1" "2" "3" "4" ...
.. ..$: chr [1:3] "PC1" "PC2" "PC3" - attr(*, "class")= chr "prcomp"  Let’s look at rotation first. As noted, each principal component (PC) is a linear combination of the input features. These coefficients are stored in rotation: > z$rotation
PC1         PC2         PC3
Height -0.05934555 -0.11013610 -0.99214321
Weight -0.99776279 -0.02410352  0.06235738
Age    -0.03078194  0.99362420 -0.10845927


For instance,

PC2 = -0.11 Height - 0.02 Weight + 0.99 Age

As noted, those 3 new variables are stored in the x component of z. For instance, consider the first person in the dataset:

> mlb[1,]
Height Weight    Age
74.00 180.00  22.99 

In the new data, his numbers are:

> z$x[1,] PC1 PC2 PC3 21.458611 -5.201495 -1.018951  Let’s check! Since the PCi are linear combinations of the original columns, we can compute them via matrix multiplication. Let’s do so for PC2, say for the first row of the data. # remember, prcomp did centering, so we need it here > mlbc <- center(mlb) > mlbc[1,] %*% z$rotation[,2]
[,1]
[1,] -5.201495

Ah yes, same as above.

## Key properties

The key properties of PCA are that the PCs

1. are arranged in order of decreasing variances, and

2. they are uncorrelated.

The variances (actually standard deviations) are reported in the return object from prcomp:

> z$sdev [1] 20.869206 4.288663 1.911677 Yes, (a) holds. Let’s double check, say for PC2: > sd(z$x[,2])
[1] 4.288663

Yes.

What about (b)?

> cor(z$x) PC1 PC2 PC3 PC1 1.000000e+00 -1.295182e-16 2.318554e-15 PC2 -1.295182e-16 1.000000e+00 2.341867e-16 PC3 2.318554e-15 2.341867e-16 1.000000e+00 Yes indeed, those new columns are uncorrelated. ## Practical importance of (a) and (b) The reader of this document has probably seen properties (a) and (b) before. But why are they so important? Many data analysts, e.g. social scientists, use PCA to search for patterns in the data. In the ML context, though, our main interest is prediction. Our focus: If we have a large number of predictor variables, we would like to reduce that number, in order to avoid overfitting, reduce computation and so on. PCA can help us do that. Properties (a) and (b) will play a central role in this. Toward that end, we will first introduce another dataset, and then discuss dimension reduction–reducing the number of predictor variables–in the context of that data. That will lead us to the importance of properties (a) and (b). ## Example: fiftyksongs data Here we will use another built-in dataset in qeML, a song database named fiftyksongs. It is a 50,000-row random subset of the famous Million Song Dataset. The first column of the data set is the year of release of the song, while the other 90 are various audio measurements. The goal is to predict the year. > dim(fiftyksongs) [1] 50000 91 > w <- prcomp(fiftyksongs[,-1]) # first column is "Y", to be predicted > w$sdev
[1] 2127.234604 1168.717654  939.840843  698.575319  546.683262  464.683454
[7]  409.785038  395.928095  380.594444  349.489142  333.322277  302.017413
[13]  282.819445  260.362550  255.472674  248.401464  235.939740  231.404983
[19]  220.682026  194.828458  193.645669  189.074051  187.455170  180.727969
[25]  173.956554  166.733909  156.612298  151.194556  144.547790  138.820897
[31]  133.966493  124.514162  122.785528  115.486330  112.819657  110.379903
[37]  109.347994  106.551231  104.787668   99.726851   99.510556   97.599960
[43]   93.161508   88.559160   87.453436   86.870468   82.452985   80.058511
[49]   79.177031   75.105451   72.542646   67.696172   64.079955   63.601079
[55]   61.105579   60.104226   56.656737   53.166604   52.150838   50.515730
[61]   47.954210   47.406341   44.272814   39.914361   39.536682   38.653450
[67]   37.228741   36.007748   34.192456   29.523751   29.085855   28.387604
[73]   26.325406   24.763188   22.192984   20.203667   19.739706   18.453111
[79]   14.238237   13.935897   10.813426    9.659868    8.938295    7.725284
[85]    6.935969    6.306459    4.931680    3.433850    3.041469    1.892496

### Dimension reduction

One hopes to substantially reduce the number of predictors from 90. But how many should we retain? And which ones?

There are 290 possible sets of predictors to use. It of And given the randomness and the huge number of sets, the odds are high that the “best”-predicting set is just an accident, not the actual best and maybe not even close to the best. course would be out of the question to check them all. So, we might consider the below predictor sets, say following the order of the features (which are named V2, V3, V4,…)

V2 alone; V2 and V3; V2 and V3 and V4; V2 and V3 and V4 and V5; etc.

Now we have only 90 predictor sets to check–a lot, but far better than 290. Yet there are two problems that would arise:

• Presumably the Vi are not arranged in order of importance as While the set V12, V13,…, V88 would include these three variables, we may risk overfitting, masking the value of these three.. predictors. What if, say, V12, V28 and V88 make for an especially powerful predictor set? The scheme considered here would never pick that up.

• Possibility of substantial duplication: What if, say, V2 and V3 are very highly correlated? Then once V2 is in our predictor set, we probably would not want to include V3; we are trying to find a parsimonious predictor set, and inclusion of (near-)duplicates would defeat the purpose. Our second candidate set above would be V2 and V4, the third would be V2 and V4 and V5; and so on. We may wish to skip some other Vi as well. Checking for such correlation at every step would be cumbersome and time-consuming.

Both problems are addressed by using the PCs Pi instead of the original variables Vi. We then consider these predictor sets,

P1 alone; P1 and P2; P1 and P2 and P3; P1 and P2 and P3 and P4; etc.

What does this buy us?

• Recall that Var(Pi) is decreasing in i (technically nonincreasing). For large i, Var(Pi) is typically tiny; in the above example, for instance, Var(P49) / Var(P1) is only about 0.0014. And a random variable with small variance is essentially constant, thus of no value as a predictor.

• By virtue of their uncorrelated nature, the Pi basically do not duplicate each other. While it is true that uncorrelatedness does not necessarily imply independence, again we have a reasonable solution to the duplication problem raised earlier.

# And What about UMAP?

Again, PCA forms new variables that are linear functions of the original ones. That can be quite useful, but possibly constraining. In recent years, other dimension reduction method have become popular, notably t-SNE and UMAP. Let’s take a very brief look at the latter.

library(umap)
mypars <- umap.defaults
mypars\$n_components <- 6
umOut <- umap(fiftyksongs[,-1],
config=mypars)

The new variables will then be returned in umOut$$layout**, analogous to our **z$$x above. We will now have a 50000 x 6 matrix, replacing our original 50000 x 90 data.

So, what does UMAP actually do? The math is quite arcane; even the basic assumption, “uniform distribution on a manifold,” is beyond the scope of this tutorial.

But roughly speaking, the goal is to transform the original data, dimension 90 here, to a lower-dimensional data set (6 here) in such a way that “local” structure is retained. The latter condition means that rows in the data that were neighbors of each other in the original data are likely still neighbors in the new data, subject to the hyperparameter n_neighbors: a data point v counts as a neighbor of u only if v is among the n_neighbors points to u.

In terms of the Bias-Variance Tradeoff, smaller values of n_neighbors reduce bias while increasing variance, in a similar manner to the value of k in the k-Nearest Neighbors predictive method.