Embark on a journey into non-linear dimensionality reduction, with a specific focus on t-Distributed Stochastic Neighbor Embedding (t-SNE). Our goal is to understand the theory behind t-SNE and apply it using R's Rtsne package. This journey will take us through an understanding of the difference between linear and non-linear dimensionality reduction, a grasp of the core concepts of t-SNE, an implementation of t-SNE using the Rtsne package, and a discussion of potential pitfalls of t-SNE.
Linear vs. Non-Linear Dimensionality Reduction
Dimensionality reduction is a pragmatic exercise which seeks to condense the number of random variables under consideration, thus obtaining a set of principal variables. By familiarizing ourselves with the dimension, we can select the technique that best suits our needs.
Imagine having a dataset that contains a person's height in inches and centimeters. These two measurements convey the same information, so one can be removed. This is an example of linear dimensionality reduction. Unlike PCA, a popular linear technique, non-linear techniques like t-SNE adopt a different approach, capturing complex relationships by preserving distances and separations, irrespective of the dimension space.
Understanding t-SNE: High-dimensional Space Calculations
t-SNE aims to keep similar data points close and dissimilar ones far apart in a lower-dimensional space. It achieves this by minimizing a cost function over the locations of the points in the lower-dimensional space.
The Gaussian joint probability is mathematically defined as:
pj∣i
Understanding t-SNE: Low-dimensional Space Calculations
In the lower-dimensional map, t-SNE employs t-distributions. These distributions, which are heavier-tailed, favor more effective modeling of dissimilarities. The joint probabilities in the low-dimensional space are defined as:
qij=
Implementing t-SNE: R Implementation
Now, let's see how to implement t-SNE in R using the Rtsne package, a popular tool for t-SNE in R. We'll use the classic Iris dataset, which contains measurements of iris flowers from three different species. We'll build a t-SNE model using Rtsne, apply it to our data, and visualize both the original and reduced data using ggplot2 with facets for a clear side-by-side comparison.
R Sample code for t-SNE and Analysis
Let's walk through the process step by step, starting with a look at the original dataset, then applying t-SNE, and finally visualizing and comparing the results. We'll also include print statements to help you understand how the data transforms at each stage.
In this code, we:
Load the Iris dataset and display the first few rows to remind you of its structure.
Pitfalls when Using t-SNE
Though modern and effective, t-SNE comes with its share of pitfalls. Firstly, interpreting the global structure can be challenging due to disagreements between the different preservation features in t-SNE. Secondly, reproducibility presents a challenge due to random initialization, which can lead to varied results across different t-SNE runs. Finally, t-SNE is sensitive to hyperparameters such as perplexity and learning_rate, whose tuning will be covered in later lessons.
Lesson Summary and Practice
Great job! We've distinguished between linear and non-linear dimensionality reduction and explored t-SNE. We've covered practical lessons in implementing t-SNE with R's Rtsne package and have had discussions on potential pitfalls that might arise. In future lessons, we will focus on visualizing t-SNE results, delving into t-SNE's parameter tuning, and exploring its application with real-world examples. Let's continue to deepen your understanding in the next stage of this educational journey!
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=
∑k=ie−(∥xi−xk∥2/2σi2)e−(∥xi−xj∥2/2σi
Here, pj∣i is the probability of xi being a neighbor of xj, given the similarity of xi to other points and σi is the variance of the Gaussian distribution. The variance is determined by the perplexity parameter, which controls the number of neighbors considered for each point.
From the conditional distributions created we calculate the joint probability distribution, using the following equation:
pij=2Npj∣i+pi∣j
Where N is the number of data points. This joint probability distribution is used to calculate the similarity between points in the high-dimensional space. Using the joint probability distribution rather than the conditional probability distribution helps to avoid clumping of points in the lower-dimensional space.
t-SNE constructs probability distributions in such a way that joint probabilities of similar points are high, while joint probabilities of dissimilar points are low. This is achieved by minimizing the Kullback-Leibler divergence between the joint probabilities in the high-dimensional space and the low-dimensional space.
∑k=l(1+∣∣yk−yl∣∣2)−1(1+∣∣yi−yj∣∣2)−1
Here, qij is the probability of yi being a neighbor of yj. t-SNE minimizes the divergence between the two distributions with respect to the locations of points (y) in the map. Here are the steps involved in the t-SNE algorithm:
Compute the pairwise similarities in the high-dimensional space using Gaussian joint probabilities using the formula for pj∣i.
Compute the pairwise similarities in the low-dimensional space using t-distributions using the formula for qij. Note that the t-distribution has a heavier tail than the Gaussian distribution and is more robust to outliers — It helps to avoid clumping of points.
Minimize the divergence between the two distributions by adjusting the locations of the points in the low-dimensional space using gradient descent.
Repeat the process until the distributions are similar.
t-SNE uses Kullback-Leibler divergence to measure the difference between the two distributions. Kullback-Leibler divergence is a measure of how one probability distribution diverges from a second, expected probability distribution. The cost function is defined as follows:
C=KL(P∣∣Q)=∑i∑jpijlogqijpij
Here, P is the joint probability distribution in the high-dimensional space, Q is the joint probability distribution in the low-dimensional space, and C is the cost function. The goal is to minimize the cost function by adjusting the locations of the points in the low-dimensional space.
Check for and count duplicate rows.
Remove duplicate rows, which is required because Rtsne cannot handle duplicates. Duplicates can cause the algorithm to get stuck or produce misleading embeddings, as they result in zero distances between points, interfering with probability calculations and optimization.
Show the first few rows of the unique data.
Run t-SNE and display the first few rows of the resulting embedding to illustrate the transformation.
Prepare two data frames: one for the original data (using the first two features) and one for the t-SNE embedding.
Combine these into a single data frame and use ggplot2 with facet_wrap to visualize both the original and t-SNE-reduced data side by side, colored by species.
