An introduction to self-organizing maps

This post presents the classical self-organizing map algorithm proposed by Grossberg [1] and Teuvo Kohonen [2]. We explain the fundamental aspects of the algorithm and its applications, and we offer a basic implementation in Pytorch.

Introduction⌗

Let us begin with a prevalent problem in science. We often have to deal with data that live in a high-dimensional space $\mathcal{X} \in \mathbb{R}^k$. When $k > 3$, things get rough for people who need help to visualize what’s happening. Moreover, many algorithms we use daily cannot operate fast enough in such high-dimensional spaces [3, 4]. Therefore, we rely on methods that reduce the dimensionality without compromising or losing much information.

One such method is a self-organizing map or SOM. SOM is an unsupervised learning algorithm that maps a high-dimensional space into a lower-dimensional one through an artificial neural network. In a more mathematical context, the idea behind an unsupervised learning problem and particularly a self-organizing map, is to learn the input distribution, meaning we are looking at an approximation for the joint distribution $p(x, y)$, where $x$ represents input samples and $y$ the output, without making any assumptions about causality.

Applications⌗

We have already seen the dimensionality reduction problem, where the data we handle are usually high-dimensional, rendering their analysis, processing, or interpretation hard. Therefore, we can use a SOM to map the original input space to a low-dimensional space and work with the reduced data [2]. Another feature of the SOM is that it retains the topographic properties of the input distribution (space). The code words are topographically organized, meaning that the neighboring units of the map represent data that are nearby in the input space. For instance, classifying world poverty is a problem where clusters (or groups of units/neurons) represent poor, rich, or in-between countries [http://www.cis.hut.fi/research/som-research/worldmap.html]. There are many other applications, but providing an exhaustive list is out of the scope of this post.

Notation and Terminology⌗

Before diving into the details and the implementation of the SOM algorithms, we must define some mathematical quantities and determine the terminology we will use. The SOM is implemented as a neural network $\mathcal{F}({\bf \theta})$ that maps a high-dimensional manifold $ \mathcal{X} \in \mathbb{R}^k $ to a low-dimensional predefined latice $\mathcal{Y} \in \mathbb{R}^m$, where $ m \ll k $. The input to the SOM is a vector $ {\bf x} \in \mathcal{X} $ and the weights, $ {\bf w} $ of the neural network that learn the representations are called code words and the set of the code words, $ \mathcal{W} = {\bf w_1, \ldots, w_n} $, is called a codebook. The cardinal number of $\mathcal{W}$ is the number of units we use in the low-level lattice or the number of neural network’s units. We let $ d(\cdot, \cdot) $ be the Minkowski distance defined as $$ d({\bf x}, {\bf y}) = \Big( \sum_{i=1}^{k} |x_i - y_i|^p \Big)^{\frac{1}{p}}, \qquad (1) $$ where here ${\bf x}, {\bf y} \in \mathbb{R}^n $ and $ p \geq 1$. For different values of $ p $, we recover different known distance functions such as the Euclidean distance ($p=2$) or the Manhattan ($p=1$).

The SOM Algorithm⌗

We are now ready to introduce the SOM algorithm. First, we will describe the main idea behind the SOM algorithm in plain words, and then we will give the pseudocode and its implementation along with some examples.

The first step is to initialize the codebook; we usually do that by assigning random values to the code words. Once we initialize the codebook, we start the learning iteration. At each iteration or epoch, we draw a sample ${\bf x}$ from the input space $ \mathcal{X} $, and we compute the distance of that sample from all the code words. We select the code word with the smallest distance from ${\bf x}$ such that $$ i_{\text{BMU}} = \text{BMU} = \text{arg}\min_{i\in \mathbb{N}^+} \left\{ d({\bf x}, {\bf w}^t_i) \right\}, \qquad (2) $$

and we call that unit (neuron) the best matching unit (BMU) or the winner unit.

Figure 1 shows a random initialization of the codebook (black grid) that contains two-dimensional code words ($ \mathcal{X} \in \mathbb{R}^2$). The white disc represents the sample ${\bf x}$ we draw. After we compute all the distances between that sample and all the grid points, we observe that the closest point of the grid to that sample is the one at the center of the yellow disc.

So now the next step is to update the code words ${\bf w}_i$ ($i = 1, \ldots, n$) by moving them towards the sample ${\bf x}$ according to the following equation:

$$ {\bf w}_i^{t+1} = {\bf w}_i^t + \epsilon(t) h(t, \text{BMU}, \sigma(t))({\bf x} - {\bf w}_i^t), \qquad(3) $$

and updating the code words that fall within a neighborhood defined by the function:

$$ h(t, \text{BMU}; \sigma(t)) = \exp\Big( \Big( \frac{d({\bf w}^t_{\text{BMU}}, {\bf w}^t_i)}{\sigma(t)} \Big)^2 \Big), \qquad(4) $$

where $t$ is the current iteration (or time step or epoch). $\epsilon(t)$ and $\sigma(t)$ are the time-dependent learning rate and neighborhood function’s variance, respectively.They both decay exponentially based on $ z_i \Big( \frac{z_f}{z_i} \Big)^{\frac{t}{t_f}} $, where $z_i$ and $z_f$ are the initial and final values, respectively ($z = {\epsilon, \sigma}$). We assume that $z_i \gg z_f$ for the initial and final values. The idea behind a time-dependent decaying learning rate is to freeze learning after some time, and a decaying variance of the neighborhood function guarantees that as the learning progresses, the number of units that get to update their weights decreases. Moreover, at the beginning of learning the radius of the neighborhood function should be large enough, and at the end of learning, we should keep the radius small such that only one or zero neighbors are within the immediate vicinity of the BMU.

Figure 2 shows the pseudocode of the SOM algorithm. The algorithm presented here is an online learning algorithm (or sequential), meaning that one sample of data is processed at each iteration. A different version of the SOM algorithm runs offline and uses the entire data set at each iteration, called batch SOM. In this post, we will implement the online algorithm used in our examples.

Pytorch Implementation⌗

The source code below is the implementation of the SOM algorithm given in Figure 2. The implementation is a simple translation of the pseudocode to Python. In this implementation, we contain all the parameters and the codebooks in $[0, 1]^k$, where $k \in \mathbb{N}^+$. We also consider the neighborhood function a Gaussian, and the final time $t_f = 1$. The metric $d(\cdot, \cdot)$ is the Euclidean distance ($p=2$ in Equation 1).

import numpy as np
import matplotlib.pylab as plt

import torch
from torch import nn


class SOM(nn.Module):
    def __init__(self,
                 n_units=16,
                 dim=2,
                 iters=1000,
                 lrate=(0.5, 0.01),
                 sigma=(0.5, 0.01)):
        super().__init__()
        self.n_units = n_units				# Number of units (n in main text)
        self.dim = dim						# Code words dim (k in main text)
        self.iters = iters
        self.lrate_i = lrate[0]
        self.lrate_f = lrate[1]
        self.sigma_i = sigma[0]
        self.sigma_f = sigma[1]

		# Initialize the code words
        self.initCodebook()
        
		# Initialize a two-dimensional grid
        self.initLatice()

		# Pre-compute the time array, learning rate, and variance
        self.t = torch.linspace(0, 1, steps=iters)
        self.lrate = self.lrate_i * (self.lrate_f / self.lrate_i)**self.t
        self.sigma = self.sigma_i * (self.sigma_f / self.sigma_i)**self.t

    def initCodebook(self):
    	""" Initialize the code words using random values drawn from a 
    	uniform distribution in [0, 0.01).
    	"""
        self.codebook = torch.ones([self.n_units*self.n_units, self.dim])
        nn.init.uniform_(self.codebook, a=0, b=0.01)

    def initLatice(self):
    	""" Initialize a two-dimensional grid (latice) on which units
    	(or neurons) are placed. Moreover, computes the distances between all
    	the pairs within the grid.
    	
    	"""
        line = torch.linspace(0, 1, steps=self.n_units)
        grid_x, grid_y = torch.meshgrid(line, line, indexing="ij")
        p = torch.cat((grid_x.flatten().reshape(-1, 1),
                       grid_y.flatten().reshape(-1, 1)), dim=1)
        self.dist = torch.cdist(p, p)

    def fit(self, X):
    	""" Fit the input data X to a two-dimensional SOM.
    	"""
        for t in range(self.iters):
        	
        	# Sample the input space
        	x = X[np.random.randint(X.shape[0])]			

			# Find the BMU
            BMU = torch.argmin((((self.codebook - x))**2).sum(dim=-1))

			# Compute the neighborhood function
            h = torch.exp(-(self.dist[BMU]/self.sigma[t])**2).unsqueeze(1)

			# Update the codebook
            self.codebook -= self.lrate[t] * h * (self.codebook - x)

Examples⌗

Square in $ \mathbb{R}^2 $⌗

Our first example is to map a two-dimensional rectangular distribution in the $[0, 1] \times [0, 1]$. To this end, we randomly generate points $(x_1, x_2) \in [0, 1] \times [0, 1]$ from a uniform distribution $\mathcal{U}$. After running the code presented earlier for $3000$ iterations and with $\epsilon_i = \sigma_i = 0.5$ and $\epsilon_f = \sigma_f = 0.01$, we obtain the self-organized map illustrated in Figure 3A, where the red dots represent the input space (and thus the samples we used to train our neural network). The white discs are the units of our SOM.

Ring in $ \mathbb{R}^2 $⌗

The second example is again related to space $[0, 1] \times [0, 1] $, only this time we draw uniformly points from a ring, rendering this problem a little a bit harder for the SOM algorithm since there is a hole in the center of our distribution. Figure 3B shows the input space distribution in red dots, and again, we see that the SOM algorithm has captured the input space. The code words (white discs) cover the space, and the lattice (black solid lines) are well organized. However, we see some units representing the hole (void) in the middle of the ring. That is a known problem with Kohonen’s SOM algorithm. The parameters for this experiment are the same as before.

Evaluate a SOM⌗

When measuring the quality of a self-organized map, there is no one measure to rule them all. People use many different measures in the literature to measure how good or how bad a self-organized map is. Three well-known measures are:

• The quantization error (or distortion), which is the average distance of the minimum of all the pairwise distances between the input space samples $\mathcal{X}$ and the codebook $\mathcal{W}$, $ \mathcal{E_{Q}} = \mathbb{E}[\min\left\{ \mathcal{D}(\mathcal{X}, \mathcal{W}) \right\} $. The quantization error does not measure how well the map preserves the data’s topology but instead measures the goodness-of-fit of the input distribution by the map. The lower the $\mathcal{E_Q}$, the better.

• The topographic error quantifies how well the map captures the shape of the input data and how well the topology of the map preserves the input space. The topographic error is computed as $\mathcal{E_{T}} = \frac{1}{|\mathcal{X}|} \sum_{{\bf x} \in \mathcal{X}}^{}I({\bf x}) $, where $I({\bf x}) = 1$ when the BMU and the second best matching unit are neighbors, and zero otherwise. The lower the value of the $\mathcal{E_{T}}$, the better.

In our examples above, we measure the quantization and topographic errors. As Table 1 shows, the errors for the square distribution are indeed minor, meaning that the maps capture and preserve the topology of the input distribution. However, the quantization error is not that small for the ring distribution, meaning that the map preserves the topology since the topographic error is small. Still, the map needs to fit the data distribution better. We can see that the map also tries to find non-existent data in the ring’s hole.

@article{detorakis2023som,
  title   = "An introduction to self-organizing maps",
  author  = "Georgios Is. Detorakis",
  journal = "gdetor.github.io",
  year    = "2023",
  url     = "https://gdetor.github.io/posts/som"
}