avsnitt 6, Ämne 10
Pågår
augusti 4, 2020
avsnitt Progress
0% färdig

We have seen how principal component analysis (PCA) can be used in the dimensionality reduction task—reducing the number of features of a dataset while maintaining the essential relationships between the points. While PCA is flexible, fast, and easily interpretable, it does not perform so well when there are nonlinearrelationships within the data; we will see some examples of these below.

To address this deficiency, we can turn to a class of methods known as manifold learning—a class of unsupervised estimators that seeks to describe datasets as low-dimensional manifolds embedded in high-dimensional spaces. When you think of a manifold, I’d suggest imagining a sheet of paper: this is a two-dimensional object that lives in our familiar three-dimensional world, and can be bent or rolled in that two dimensions. In the parlance of manifold learning, we can think of this sheet as a two-dimensional manifold embedded in three-dimensional space.

Rotating, re-orienting, or stretching the piece of paper in three-dimensional space doesn’t change the flat geometry of the paper: such operations are akin to linear embeddings. If you bend, curl, or crumple the paper, it is still a two-dimensional manifold, but the embedding into the three-dimensional space is no longer linear. Manifold learning algorithms would seek to learn about the fundamental two-dimensional nature of the paper, even as it is contorted to fill the three-dimensional space.

Here we will demonstrate a number of manifold methods, going most deeply into a couple techniques: multidimensional scaling (MDS), locally linear embedding (LLE), and isometric mapping (IsoMap).

We begin with the standard imports:In [1]:

%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np


Manifold Learning: ”HELLO”

To make these concepts more clear, let’s start by generating some two-dimensional data that we can use to define a manifold. Here is a function that will create data in the shape of the word ”HELLO”:In [2]:

def make_hello(N=1000, rseed=42):
# Make a plot with "HELLO" text; save as PNG
fig, ax = plt.subplots(figsize=(4, 1))
ax.axis('off')
ax.text(0.5, 0.4, 'HELLO', va='center', ha='center', weight='bold', size=85)
fig.savefig('hello.png')
plt.close(fig)

# Open this PNG and draw random points from it
rng = np.random.RandomState(rseed)
X = rng.rand(4 * N, 2)
i, j = (X * data.shape).astype(int).T
mask = (data[i, j] < 1)
X[:, 0] *= (data.shape[0] / data.shape[1])
X = X[:N]
return X[np.argsort(X[:, 0])]


Let’s call the function and visualize the resulting data:In [3]:

X = make_hello(1000)
colorize = dict(c=X[:, 0], cmap=plt.cm.get_cmap('rainbow', 5))
plt.scatter(X[:, 0], X[:, 1], **colorize)
plt.axis('equal');


The output is two dimensional, and consists of points drawn in the shape of the word, ”HELLO”. This data form will help us to see visually what these algorithms are doing.

Multidimensional Scaling (MDS)

Looking at data like this, we can see that the particular choice of x and y values of the dataset are not the most fundamental description of the data: we can scale, shrink, or rotate the data, and the ”HELLO” will still be apparent. For example, if we use a rotation matrix to rotate the data, the x and y values change, but the data is still fundamentally the same:In [4]:

def rotate(X, angle):
R = [[np.cos(theta), np.sin(theta)],
[-np.sin(theta), np.cos(theta)]]
return np.dot(X, R)

X2 = rotate(X, 20) + 5
plt.scatter(X2[:, 0], X2[:, 1], **colorize)
plt.axis('equal');


This tells us that the x and y values are not necessarily fundamental to the relationships in the data. What is fundamental, in this case, is the distancebetween each point and the other points in the dataset. A common way to represent this is to use a distance matrix: for $N$ points, we construct an $N \times N$ array such that entry $(i, j)$ contains the distance between point $i$ and point $j$. Let’s use Scikit-Learn’s efficient pairwise_distances function to do this for our original data:In [5]:

from sklearn.metrics import pairwise_distances
D = pairwise_distances(X)
D.shape


Out[5]:

(1000, 1000)

As promised, for our N=1,000 points, we obtain a 1000×1000 matrix, which can be visualized as shown here:In [6]:

plt.imshow(D, zorder=2, cmap='Blues', interpolation='nearest')
plt.colorbar();


If we similarly construct a distance matrix for our rotated and translated data, we see that it is the same:In [7]:

D2 = pairwise_distances(X2)
np.allclose(D, D2)


Out[7]:

True

This distance matrix gives us a representation of our data that is invariant to rotations and translations, but the visualization of the matrix above is not entirely intuitive. In the representation shown in this figure, we have lost any visible sign of the interesting structure in the data: the ”HELLO” that we saw before.

Further, while computing this distance matrix from the (x, y) coordinates is straightforward, transforming the distances back into x and y coordinates is rather difficult. This is exactly what the multidimensional scaling algorithm aims to do: given a distance matrix between points, it recovers a $D$-dimensional coordinate representation of the data. Let’s see how it works for our distance matrix, using the precomputed dissimilarity to specify that we are passing a distance matrix:In [8]:

from sklearn.manifold import MDS
model = MDS(n_components=2, dissimilarity='precomputed', random_state=1)
out = model.fit_transform(D)
plt.scatter(out[:, 0], out[:, 1], **colorize)
plt.axis('equal');