Episode 20: What Is Dimensionality Reduction? A Gentle Explanation of How AI Learns More Effectively by Simplifying Data

Is Too Much Information a Problem for AI? Understanding Dimensionality Reduction

In the world of data, having “a lot of information” isn’t always a good thing. For example, if you want to predict how well a product will sell, you might gather various factors like weather, day of the week, whether there was advertising, location, past sales, and more. At first glance, it may seem like “the more data, the better the prediction.”

However, in reality, trying to handle too many elements at once can confuse AI or make it harder to find important patterns. When things get overly complex, the key features can get buried.

Dimensionality reduction is a technique that helps organize this kind of “information overload” by keeping only what truly matters and simplifying the rest. Although the term may sound technical, its role is surprisingly familiar and even relates to how humans make decisions.

What Does “Dimension” Mean? How It Helps AI Learn Better

Put simply, dimensionality reduction means selecting only the most important features from data with many characteristics and re-expressing it using fewer elements.

Here, “dimension” refers to the number of aspects or viewpoints in the data. For instance, when analyzing a person’s face photo with AI, that image consists of thousands of pixels. If each pixel is considered one “dimension,” then we’re dealing with very high-dimensional data.

Such high-dimensional data not only takes longer to process but also tends to include unnecessary information called “noise.” That’s where dimensionality reduction comes in.

Famous methods include PCA (Principal Component Analysis) and t-SNE (t-distributed Stochastic Neighbor Embedding). While their mechanisms differ, they share the same goal: extracting meaningful structure from data and compressing it.

Like Maps or Self-Introductions? Understanding Through Familiar Examples

Think of it like folding a large map into pocket size. You may not see every detail on the full map anymore, but as long as you know where you are and where you need to go next, that’s enough. In the same way, AI becomes more efficient at learning and predicting when it focuses only on necessary information.

Another relatable example is writing a self-introduction or personal statement. In job hunting or similar situations, instead of listing every experience or skill you have, it’s more effective to highlight just a few key points that truly represent you. Similarly for AI—by identifying which features really matter—it becomes easier for it to perform at its best.

That said, this technique does come with caveats. There’s always a chance that some genuinely important information might be lost during dimensionality reduction. Also, since data gets transformed into forms that are harder for humans to interpret, it can become difficult to explain “why” an AI made a certain decision.

This issue ties closely with an area called Explainable AI—a field focused on making sure humans can understand why an AI reached its conclusions or predictions. This will become increasingly important going forward.

Dimensionality Reduction as a Skill in Choosing What Matters

Even so, this idea of “tidying up” is essential when working with high-dimensional data. Especially today—with growing use of images and audio that contain vast amounts of information—dimensionality reduction has become more important than ever.

Data analysis might sound intimidating at first. But at its core lies something simple and very human: focusing on what truly matters. And perhaps that’s wisdom we can apply not just in technology but in our everyday lives as well.

Choosing what to keep requires courage—but doing so often reveals new insights. If this quiet yet powerful idea came through clearly from this article, then I’m glad.