# A practical guide to dimensionality reduction techniques

Practical examples of common dimensionality reduction algorithms in Python

Dimensionality reduction is a way to simplify complex datasets to make working with them more manageable. As data grows in size and complexity, it becomes increasingly difficult to draw meaningful insights, and even more difficult to visualize. Dimensionality reduction techniques help resolve this issue by giving you a smaller number of dimensions (columns) to work with, while still preserving the most important information. Think of it like casting a shadow of a complex object - you lose some detail, but you gain a simpler representation that's easier to work with and make comparisons with.

In this article, we will demonstrate how to implement various linear and non-linear dimensionality reduction algorithms in Python and visualize the differences between them.

Throughout this article, code snippets are presented alongside real, embedded outputs from the companion Hex Project, which you're encouraged to duplicate and use to follow along.

## The data

The dataset used for these examples is a 🍷 wine dataset consisting of 13 features, or dimensions, which represent 3 different types of wines. Unfortunately for us, the specific wine types are unknown.

The goal here is to use dimensionality reduction along with the Kmeans clustering algorithm to reveal the wine groups hidden amongst our dataset. Let's take a look at the raw data.

We'll do some very light preprocessing to get the data into a workable state.

It's always good to check if there's any null values in the dataset:

``data.isnull().sum()``

Ours has none, so I haven't bothered printing out the long list of 0's that code spat out.

We'll also quickly standardize the data, which maps every column to a similar range and scale— this is important if you want to compare columns to one another directly.

``````from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data = pd.DataFrame(data = scaler.fit_transform(data), columns = data.columns)

## Linear methods

Linear techniques aim to simplify the data while preserving the most important information. They do this by finding a way to represent the data in a lower-dimensional space that captures the original patterns and linear relationships in the data. The techniques we'll be looking at in this tutorial are:

• PCA

• ICA

• TruncatedSVD

### Principal component analysis (PCA)

Principal component analysis, or PCA, reduces the number of dimensions in your dataset while maximizing the explained variance per component. So... what does that actually mean? In every dataset, each column describes something. That something could be height, weight, or in our case it could be alcohol content. In any case, variance is a way to measure how much the values in a column fluctuate. The more variance a feature has, the more information that feature contains. Features with 0 variance add no information, making variance our friend.

PCA works by finding a compressed representation of our original data in a lower dimension that maximizes the overall variance (or fluctuations) of the original data. This means that the overall patterns are still preserved while making the data as simple as possible.

This code implements a PCA in Python:

``````from sklearn.decomposition import PCA

# Reduction objects
reducer3D = PCA(n_components=3)
reducer2D = PCA(n_components=2)

# compressed data in 2 and 3 dimensions
reduced_data3D = reducer3D.fit_transform(data)
reduced_data2D = reducer2D.fit_transform(data)

# creates a dataframe to visualize each reduced version
reduced3D_df = pd.DataFrame(data =  reduced_data,
columns = ['component_1', 'component_2', 'component_3'])
reduced2D_df = pd.DataFrame(data =  reduced_data,
columns = ['component_1', 'component_2'])

# Clustering allows us to add distinct colors to each group in our plot
kmeans = KMeans(n_clusters = 3)
reduced2D_df['cluster'] = kmeans.fit_predict(reduced2D_df)
reduced3D_cluster = kmeans.fit_predict(reduced3D_df)``````

We can see the data nicely forms three clusters in both 3 and 2 dimensions:

### Independent Component Analysis (ICA)

The goal of Independent Component Analysis (ICA) is to separate mixed signals into their original sources. In ICA, we assume that the sources are independent from each other, meaning they don't affect each other's behavior. Let's take a simple example to understand this.

In our dataset, we have 3 different types of wine flavors present and we want to separate them back into their original wine types. Even though the wines are currently mixed, we believe that they have nothing to do with each other statistically. This basically means that if we were to drink one flavor of wine, we wouldn't gain any extra information about the other flavors. Assuming each flavor is independent, ICA will untangle the mixed wines and recover their original flavors, although you had no prior knowledge of what flavors were mixed or how they were mixed in the first place.

After selecting the number of desired independent components (flavors of wine), you can use them as a reduced representation of your data. These components, which are statistically independent, capture different aspects of the original data, allowing for a reduced representation.

This code implements an ICA in Python:

``````from sklearn.decomposition import FastICA

# Reduction objects
reducer3D = FastICA(n_components=3)
reducer2D = FastICA(n_components=2)

# compressed data in 2 and 3 dimensions
reduced_data3D = reducer3D.fit_transform(data)
reduced_data2D = reducer2D.fit_transform(data)

# creates a dataframe to visualize each reduced version
reduced3D_df = pd.DataFrame(data =  reduced_data,
columns = ['component_1', 'component_2', 'component_3'])reduced2D_df = pd.DataFrame(data =  reduced_data,
columns = ['component_1', 'component_2'])# Clustering allows us to add distinct colors to each group in our plot
kmeans = KMeans(n_clusters = 3)
reduced2D_df['cluster'] = kmeans.fit_predict(reduced2D_df)
reduced3D_cluster = kmeans.fit_predict(reduced3D_df)``````

We can again visualize this in both 3 and 2 dimensions:

### TruncatedSVD

TruncatedSVD, short for Truncated Singular Value Decomposition, is a dimensionality reduction technique that is particularly effective when working with large datasets. It is very closely related to PCA and may even be considered a variant of PCA, although this technique is much better at creating a dense representation from a sparse matrix (a matrix with a lot of zeros).

This code implements a TruncatedSVD in Python:

``````from sklearn.decomposition import TruncatedSVD

# Reduction objects
reducer3D = TruncatedSVD(n_components=3)
reducer2D = TruncatedSVD(n_components=2)

# compressed data in 2 and 3 dimensions
reduced_data3D = reducer3D.fit_transform(data)
reduced_data2D = reducer2D.fit_transform(data)

# creates a dataframe to visualize each reduced version
reduced3D_df = pd.DataFrame(data =  reduced_data,
columns = ['component_1', 'component_2', 'component_3'])reduced2D_df = pd.DataFrame(data =  reduced_data,
columns = ['component_1', 'component_2'])

# Clustering allows us to add distinct colors to each group in our plot
kmeans = KMeans(n_clusters = 3)
reduced2D_df['cluster'] = kmeans.fit_predict(reduced2D_df)
reduced3D_cluster = kmeans.fit_predict(reduced3D_df)``````

## Non-linear methods

Non-linear dimensionality reduction techniques try to capture the more complex non-linear relationships in the data and represent them in a lower dimensional space. In this tutorial, we will be covering the three most commonly used options:

• Multidimensional scaling

• T-SNE

• UMAP

### Multidimensional Scaling (MDS)

Multidimensional scaling is a technique used to visually represent the similarity or dissimilarity between observations in a dataset. In this representation, similar observations are placed closer together, while dissimilar observations are positioned farther apart. MDS offers the advantage of performing both linear and nonlinear dimensionality reduction, depending on the specific settings and algorithm being used. In all cases, MDS aims to maintain the distances between data points, ensuring that the lower-dimensional representation preserves these distances.

``````from sklearn.manifold import MDS

# Reduction objects
manifold3D = MDS(n_components=3)
manifold2D = MDS(n_components=2)

# compressed data in 2 and 3 dimensions
manifold_data3D = manifold3D.fit_transform(data)
manifold_data2D = manifold2D.fit_transform(data)

# creates a dataframe to visualize each reduced version
manifold3D_df = pd.DataFrame(data =  manifold_data3D,
columns = ['component_1', 'component_2', 'component_3'])manifold2D_df = pd.DataFrame(data =  manifold_data2D,
columns = ['component_1', 'component_2'])

# Clustering allows us to add distinct colors to each group in our plot
kmeans = KMeans(n_clusters = 3)
manifold2D_df['cluster'] = kmeans.fit_predict(manifold2D_df)
manifold3D_cluster = kmeans.fit_predict(manifold3D_df)``````

### T-distributed Stochastic Neighbor Embedding (TSNE)

T-distributed Stochastic Neighbor Embedding (t-SNE) is an algorithm used to simplify and visualize complex data. It does this by comparing the similarities between data points in both the original high-dimensional space and a lower-dimensional space. Then, it creates probability distributions to represent these similarities, aiming to make them as similar as possible. The algorithm adjusts the positions of the data points in the lower-dimensional space iteratively until the distributions are as close as possible.

One key thing to note about t-SNE is that it focuses more on preserving local relationships rather than global ones. This means that data points that are close together in the original high-dimensional space will likely stay close together in the lower-dimensional representation. However, the overall distances between points might not be preserved. This trade off allows t-SNE to highlight local patterns and clusters, making it useful for visualizing complex data.

Here's Python code to perform a 3D and 2D t-SNE reduction on our data:

``````from sklearn.manifold import TSNE

# Reduction objects
manifold3D = TSNE(n_components=3)
manifold2D = TSNE(n_components=2)

# compressed data in 2 and 3 dimensions
manifold_data3D = manifold3D.fit_transform(data)
manifold_data2D = manifold2D.fit_transform(data)

# creates a dataframe to visualize each reduced version
manifold3D_df = pd.DataFrame(data =  manifold_data3D,
columns = ['component_1', 'component_2', 'component_3'])manifold2D_df = pd.DataFrame(data =  manifold_data2D,
columns = ['component_1', 'component_2'])

# Clustering allows us to add distinct colors to each group in our plot
kmeans = KMeans(n_clusters = 3)
manifold2D_df['cluster'] = kmeans.fit_predict(manifold2D_df)
manifold3D_cluster = kmeans.fit_predict(manifold3D_df)``````

### Uniform Manifold Approximation and Projection (UMAP)

UMAP is like t-SNE's cool older cousin. It also learns a non-linear mapping that keeps clusters intact, and it can do it faster. Additionally, UMAP tends to do a better job at preserving the global structure of the data compared to t-SNE. In this context, global structure refers to the "closeness" between similar wine types whereas local structure would refer to how well wines of the same type cluster together.

UMAP has it's own Python package, which makes it very easy to use:

``````from umap import UMAP

# Reduction objects
manifold3D = UMAP(n_components=3)
manifold2D = UMAP(n_components=2)

# compressed data in 2 and 3 dimensions
manifold_data3D = manifold3D.fit_transform(data)
manifold_data2D = manifold2D.fit_transform(data)

# creates a dataframe to visualize each reduced version
manifold3D_df = pd.DataFrame(data =  manifold_data3D,
columns = ['component_1', 'component_2', 'component_3'])manifold2D_df = pd.DataFrame(data =  manifold_data2D,
columns = ['component_1', 'component_2'])

# Clustering allows us to add distinct colors to each group in our plot
kmeans = KMeans(n_clusters = 3)
manifold2D_df['cluster'] = kmeans.fit_predict(manifold2D_df)
manifold3D_cluster = kmeans.fit_predict(manifold3D_df)``````

These examples are just scratching the surface of what's possible with dimensionality reduction, but these core techniques are extremely useful when working with any complex dataset.

Remember, dimensionality reduction is not a one-size-fits-all solution; the hardest part is figuring out which method to employ based on the nature of your data and the specific problem you're trying to solve— or if dimensionality reduction is even the right technique to use at all.

If you want to dive in and start exploring, you can duplicate the companion Hex Project to start with a template you can customize. It's free to sign up and the demo dataset is built right in, so grab a delicious glass of wine (but which kind?!) and get reducing.

This is something we think a lot about at Hex, where we're creating a platform that makes it easy to build and share interactive data products which can help teams be more impactful. If this is is interesting, click below to get started, or to check out opportunities to join our team.