The Hidden Dangers Of T-SNE: Avoid These Pitfalls!

The t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm is a powerful tool for visualizing high-dimensional data, but it comes with a set of challenges that many users overlook. Misinterpretations of t-SNE results can lead to false insights, misleading conclusions, and even flawed decision-making. In this article, we’ll uncover the hidden dangers of t-SNE, focusing on common pitfalls and how to avoid them.

t-SNE Is Not a Clustering Algorithm

Misconception: Clusters in t-SNE Indicate True Groups

One of the biggest mistakes users make is assuming that clusters in a t-SNE plot represent actual groups in the data. While t-SNE preserves local structure, it does not guarantee that the distances between clusters have any statistical meaning.

Why t-SNE Forms Clusters

t-SNE tends to force data into clusters due to the way it optimizes pairwise similarities. This means that even if the original data does not have distinct clusters, t-SNE might still display them. This can create an illusion of structure where none exists.

What to Do Instead

Use clustering algorithms like DBSCAN or K-means on the original data before interpreting t-SNE.
Compare t-SNE results with other dimensionality reduction methods like UMAP or PCA.
Never assume proximity in t-SNE space equals true similarity in the original data.

The Perplexity Trap: Choosing the Wrong Value

What Is Perplexity in t-SNE?

Perplexity is a key hyperparameter in t-SNE that controls how the algorithm balances local vs. global structure in the data. A low perplexity value focuses on fine details, while a high perplexity value considers broader patterns.

Common Pitfall: Default Perplexity Doesn’t Work for Every Dataset

Many users stick with the default perplexity (usually 30) without understanding how it affects the visualization. This can lead to misleading patterns, artificial separations, or excessive noise.

How to Tune Perplexity Properly

Try multiple values (e.g., 5, 30, 50, 100) and compare results.
Use domain knowledge to decide whether you need more global structure or more local detail.
If your dataset has fewer than 1000 samples, keep perplexity below 50 to avoid over-smoothing.

Overfitting and Sensitivity to Random Initialization

The Random Nature of t-SNE

t-SNE initializes randomly, which means different runs can produce different embeddings. This variability makes it hard to trust the stability of the visualization.

How This Leads to False Insights

A different initialization can lead to a different-looking plot, which may suggest different relationships in the data.
Overfitting small datasets: t-SNE can exaggerate minor differences if not tuned properly.
False stability: Running t-SNE once and assuming the result is absolute truth.

How to Ensure Stability

Run t-SNE multiple times with different random seeds. If the patterns remain, they are more likely to be meaningful.
Use reproducible settings (e.g., setting random_state in implementations like scikit-learn).
Consider UMAP, which is generally more stable across runs.

t-SNE Distorts Global Relationships

Why t-SNE Doesn’t Preserve Distances

t-SNE focuses on preserving local relationships, meaning distances between points do not reflect true distances in the original high-dimensional space. Large gaps or separations in t-SNE plots may be entirely artificial.

Common Pitfall: Assuming Distance in t-SNE = Real Distance

A large gap between two clusters does not necessarily mean they are truly far apart. Similarly, two points close together in t-SNE space may not be similar in high-dimensional space.

How to Avoid This Misinterpretation

Compare t-SNE with PCA or MDS to see if global relationships hold.
Use color-coding based on meaningful features to check if structure aligns with known information.
Avoid making decisions purely based on t-SNE plots.

Computational Cost and Scalability Issues

t-SNE Struggles with Large Datasets

t-SNE is computationally expensive, especially on datasets with thousands or millions of samples. The original t-SNE algorithm has O(N²) complexity, making it impractical for large-scale data visualization.

Common Pitfall: Running t-SNE on Large Data Without Optimization

Running standard t-SNE on large datasets often results in slow computation, memory issues, or poor-quality embeddings.

Solutions for Large-Scale t-SNE

Use Barnes-Hut t-SNE (fast approximation for datasets with 10k+ points).
Try FIt-SNE (Faster t-SNE) or openTSNE for better scalability.
Consider UMAP, which is much faster and often provides better global structure.

Better Alternatives to t-SNE: When to Use Other Methods

While t-SNE is a popular choice for visualizing high-dimensional data, it is not always the best option. Other methods may provide better scalability, stability, and interpretability depending on your use case.

UMAP: A More Reliable Alternative

UMAP (Uniform Manifold Approximation and Projection) is often considered a superior alternative to t-SNE because:

It is much faster (linear time complexity instead of quadratic).
It preserves more global structure while still maintaining local relationships.
It is more stable across multiple runs, reducing randomness.

When to Use UMAP Instead of t-SNE

You have large datasets (100,000+ samples).
You need to maintain both global and local structure.
You want a deterministic output that doesn’t change on every run.

PCA: Simple but Effective

Principal Component Analysis (PCA) is a linear dimensionality reduction technique that works well when data has a strong linear structure. Unlike t-SNE, PCA:

Preserves global distances between points.
Is deterministic, meaning results are consistent every time.
Is computationally cheap, scaling well for large datasets.

When to Use PCA Instead of t-SNE

You need an explainable, global transformation of the data.
Your dataset is already clustered in a linear way (e.g., gene expression data, financial data).
You want a quick preview of the data before using more complex methods.

Autoencoders: Deep Learning for Dimensionality Reduction

Neural networks can also be used for dimensionality reduction through autoencoders, which are a type of deep learning model trained to compress and reconstruct data.

When to Use Autoencoders Instead of t-SNE

You have extremely high-dimensional data (e.g., images, text embeddings).
You need a nonlinear feature extraction method.
You plan to use the learned embeddings for downstream tasks, not just visualization.

Best Practices for Using t-SNE Effectively

Even if you choose to use t-SNE, there are ways to minimize its risks and improve reliability.

1. Always Compare Multiple Runs

Because t-SNE is sensitive to randomness, run it multiple times with different seeds to check for consistency.

2. Experiment with Perplexity and Learning Rate

Low perplexity (<30): Focuses on local structures, but may create artificial clusters.
High perplexity (>50): Captures more global structure but may blur details.
Learning rate: A too-small value can lead to bad embeddings, while a too-large value can create instability.

3. Combine t-SNE with Other Methods

Use PCA before t-SNE to reduce noise and improve stability.
Compare t-SNE results with UMAP or autoencoders to verify patterns.
Apply clustering separately to avoid mistaking visual artifacts for real clusters.

4. Use Color-Coding to Enhance Interpretability

Adding metadata-based colors to a t-SNE plot can make patterns easier to understand. For example:

Label different categories (e.g., disease types, customer segments).
Use a gradient color scale for continuous variables.
Check if color patterns align with expected domain knowledge.

Real-World Use Cases: When t-SNE Works Well

Despite its pitfalls, t-SNE remains useful in certain scenarios—if applied correctly.

1. Visualizing Image Embeddings

t-SNE is widely used in computer vision to analyze feature embeddings from convolutional neural networks (CNNs).
Example:

Mapping how different image categories relate to each other in feature space.
Understanding latent representations in deep learning models.

2. Single-Cell RNA Sequencing Analysis

In bioinformatics, t-SNE helps explore gene expression patterns in single-cell RNA sequencing (scRNA-seq) data.
Example:

Identifying subpopulations of cells based on gene expression profiles.
Comparing t-SNE plots with UMAP to confirm biological relevance.

3. NLP: Word Embeddings and Topic Modeling

t-SNE is sometimes used in natural language processing (NLP) to visualize word vectors or topic distributions.
Example:

Displaying how word embeddings (e.g., from Word2Vec or BERT) cluster semantically.
Checking topic separability in text datasets.

Final Thoughts: Should You Use t-SNE?

t-SNE is not a magic bullet—it is a tool with serious limitations that require careful handling.
✅ Use it for: Small-to-medium datasets, local structure visualization, deep learning feature exploration.
❌ Avoid it for: Large datasets, clustering, global structure analysis, real-world decision-making.

If you’re looking for a more scalable, stable, and interpretable method, UMAP, PCA, and autoencoders are often better choices. Always cross-check results, avoid over-interpretation, and be mindful of the hidden dangers of t-SNE. 🚀

FAQs

Why do t-SNE plots look different each time I run them?

t-SNE is stochastic, meaning its results depend on the random initialization of data points. Running it multiple times without fixing a random seed can produce different visualizations each time.

Solution: Set a fixed random state or compare multiple runs to ensure consistency. Alternatively, use UMAP, which is more stable.

What does perplexity do in t-SNE, and how should I choose it?

Perplexity controls how much local vs. global structure is preserved.

Low perplexity (e.g., 5-30): Captures fine-grained details but can create artificial clusters.
High perplexity (e.g., 50-100): Focuses on broader structure but may oversmooth details.

Example: If you’re analyzing genetic expression data, a lower perplexity might be useful to reveal subpopulations of cells, while a higher perplexity helps understand overall gene variations.

Why are clusters appearing in t-SNE even when my data isn’t clustered?

t-SNE naturally groups similar points together, even if the original data does not have distinct clusters. This can mislead users into believing that the data has real groupings when it doesn’t.

Solution: Run a proper clustering algorithm like DBSCAN or K-Means on the original data instead of relying on t-SNE alone.

Can I use t-SNE for big datasets?

Standard t-SNE struggles with large datasets because of its O(N²) complexity, making it slow and memory-intensive.

Alternatives for large datasets:

Barnes-Hut t-SNE (efficient for datasets with 10K+ points).
FIt-SNE or openTSNE (optimized implementations).
UMAP, which scales much better while producing similar results.

Why do distances between points in t-SNE not match my original data?

t-SNE only preserves local similarities, meaning that distances in the low-dimensional plot are not reliable representations of real distances in high-dimensional space.

Example: Two species of animals might appear far apart in t-SNE, but their genetic data may suggest they are closely related. t-SNE distorts these relationships.

Can I use t-SNE for clustering?

No. t-SNE is not a clustering algorithm—it’s a visualization tool. It can make patterns appear clustered even when no real clusters exist.

Better approach: Use a proper clustering algorithm like HDBSCAN or Gaussian Mixture Models (GMM) and then apply t-SNE for visualization.

Is UMAP better than t-SNE?

UMAP is often faster, more stable, and preserves global structure better than t-SNE. Many researchers and data scientists prefer UMAP for large-scale applications. However, t-SNE might still be useful for specific highly nonlinear data.

Example: If you’re visualizing word embeddings from NLP models, UMAP often provides clearer groupings compared to t-SNE.

How do I interpret t-SNE results correctly?

Clusters are not always real. They may be an artifact of how t-SNE arranges points.
Distances are misleading. Two close points in t-SNE might be far apart in reality.
Run multiple times to check for consistency before making conclusions.
Use metadata (e.g., color-coding known labels) to verify that t-SNE results align with real-world knowledge.

Why does my t-SNE plot look like a ball or a tangled mess?

If your t-SNE plot appears as a single dense cluster or tangled shape, this usually means:

Perplexity is too high, making the algorithm consider too many distant points as neighbors.
Learning rate is too small, preventing proper separation of meaningful patterns.
The dataset lacks structure, meaning t-SNE doesn’t have natural relationships to reveal.

Solution: Try lowering perplexity, increasing the learning rate, or using PCA first to reduce noise before applying t-SNE.

Can I use t-SNE for time series data?

t-SNE does not explicitly preserve sequential relationships, making it less ideal for time-series data.

Example: If you’re analyzing stock market trends, t-SNE may distort trends by breaking continuous sequences into artificial clusters.

Better alternatives:

Use t-SNE on extracted features (e.g., autoencoder embeddings of time-series windows).
Consider t-SNE with dynamic time warping (DTW) for distance calculations.
Try UMAP, which better retains structure over time.

Why does my t-SNE plot change with different data subsets?

t-SNE’s relative positioning of points changes when you remove or add data, making it unreliable for comparing different datasets or subsets.

Example: If you run t-SNE on only half of your dataset, the structure may look completely different from when you run it on the full dataset.

Solution: If you need consistent embeddings across subsets, use UMAP or PCA, which preserve relationships more reliably.

Can I use t-SNE for feature selection?

t-SNE is a visualization tool, not a feature selection method. It does not provide weights or rankings for features.

Better approach: Use methods like LASSO regression, mutual information, or SHAP values to identify important features before applying t-SNE.

Is t-SNE useful for supervised learning?

t-SNE is not designed for supervised learning, as it does not maintain feature-label relationships.

Example: If you’re working on fraud detection, t-SNE cannot directly help with classification but may offer insights into feature separability.

Alternative: Try supervised embeddings like t-SNE with class labels (parametric t-SNE) or deep learning approaches like contrastive learning.

Why does t-SNE take so long to run?

t-SNE has quadratic time complexity (O(N²)), making it slow for large datasets.

How to speed it up:

Reduce the number of samples (e.g., apply PCA first to remove redundant data).
Use Barnes-Hut t-SNE (optimized for large datasets).
Switch to UMAP, which is significantly faster.

Can I interpret t-SNE axes like a normal plot?

No! The x and y axes in t-SNE plots have no specific meaning. They do not correspond to real-world variables or numerical relationships.

Example: If you run t-SNE on customer transaction data, the x-axis and y-axis will not represent specific spending habits. The plot only shows relative groupings.

Correct approach: Look at patterns, clusters, and color-coded metadata rather than trying to interpret axes values directly.

Can t-SNE be used for anomaly detection?

t-SNE alone is not a robust anomaly detection method, but it can help visualize outliers in high-dimensional data.

Example: In cybersecurity, t-SNE may reveal unusual login behaviors by highlighting isolated points. However, anomaly detection should be confirmed using statistical models like Isolation Forest or LOF (Local Outlier Factor).

Does t-SNE work well with categorical data?

t-SNE is designed for continuous numerical data and does not handle categorical variables directly.

Solution: Convert categorical data into numerical embeddings using:

One-hot encoding (for small categorical sets).
Word embeddings (e.g., Word2Vec, GloVe) for text data.
Feature hashing for large-scale categorical variables.

Would you like help fine-tuning t-SNE for your specific dataset? Let’s explore the best approach! 🚀