Kernel Methods and Prototype Representations

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In this chapter, you'll learn about:

• Prototype Representations: Describing a prediction problem using representative examples or class centers.
• Nearest-Neighbor Style Methods: Making predictions from local similarity in the input space.
• Feature Maps and Kernels: Extending linear models by measuring similarity in richer spaces.
• The Kernel Trick: Computing inner products in high-dimensional feature spaces without constructing those features explicitly.
• Tradeoffs: When prototype and kernel methods are powerful, and when they become expensive or fragile.

So far, most models in this section have the form

$$f(\mathbf{x}) = \mathbf{w}^\top \mathbf{x} + b$$

which means their decision boundaries are linear in the chosen features. That is often a strong baseline, but sometimes the geometry of the problem is more naturally expressed in terms of similarity:

• Which training examples look most like the new input?
• Which class prototype is closest?
• How similar are two inputs after a nonlinear feature transformation?

Prototype methods and kernel methods answer those questions directly.

Prototype-Based Thinking

A prototype is a representative point or reference example used to summarize a region of the input space.

Examples:

• a class mean in nearest-centroid classification
• a stored training example in nearest-neighbor methods
• a learned reference point in radial basis function models

The central idea is that prediction can be based on closeness to prototypes rather than only on a global linear rule.

Nearest Neighbor

The simplest prototype method is nearest neighbor.

Given a training set $\{(\mathbf{x}^{(i)}, y^{(i)})\}_{i=1}^{N}$ and a distance function $d$, the 1-nearest-neighbor prediction is:

$$\hat{y}(\mathbf{x}) = y^{(i^*)} \quad \text{where} \quad i^* = \arg\min_i d(\mathbf{x}, \mathbf{x}^{(i)})$$

This method:

• has essentially no explicit training phase
• can represent highly nonlinear decision boundaries
• depends strongly on the metric and feature scaling

k-Nearest Neighbors

Instead of using only one neighbor, k-NN uses the $k$ closest examples.

• In classification, it usually predicts by majority vote.
• In regression, it usually predicts by averaging the neighbors' targets.

Increasing $k$ usually:

• reduces variance
• increases bias

This makes $k$ itself a hyperparameter that should be selected using validation data.

Class Prototypes

Sometimes we do not want to store every training example. A cheaper approximation is to summarize each class with a small number of prototypes.

Nearest Centroid

For a class $c$, define its centroid:

$$\boldsymbol{\mu}_c = \frac{1}{N_c}\sum_{i : y^{(i)}=c}\mathbf{x}^{(i)}$$

Then predict the class whose centroid is closest:

$$\hat{y}(\mathbf{x}) = \arg\min_c d(\mathbf{x}, \boldsymbol{\mu}_c)$$

This is fast and interpretable, but it only works well when each class is reasonably compact around a center.

From Prototypes to Feature Maps

Another way to use prototypes is to convert distances to features.

Suppose we choose prototypes $\mathbf{p}_1, \dots, \mathbf{p}_K$. We can define new features such as:

$$\phi_j(\mathbf{x}) = \exp\left(-\frac{\|\mathbf{x} - \mathbf{p}_j\|^2}{2\sigma^2}\right)$$

These are radial basis features. A linear model in the transformed feature space,

$$f(\mathbf{x}) = \sum_{j=1}^{K} w_j \phi_j(\mathbf{x}) + b$$

can represent nonlinear behavior in the original input space.

This is an important bridge:

• prototypes define local regions of influence
• a linear model combines those local responses globally

Kernel Methods

Kernel methods start from a related observation: sometimes we want a linear model in a richer feature space $\phi(\mathbf{x})$, but we do not want to form $\phi(\mathbf{x})$ explicitly.

Instead, we work with a kernel function

$$K(\mathbf{x}, \mathbf{x}') = \phi(\mathbf{x})^\top \phi(\mathbf{x}')$$

which computes the inner product in that feature space directly.

Why Kernels Help

Suppose a classifier is linear in feature space:

$$f(\mathbf{x}) = \mathbf{w}^\top \phi(\mathbf{x}) + b$$

If the learned weight vector can be written as a combination of training features,

$$\mathbf{w} = \sum_{i=1}^{N} \alpha_i \phi(\mathbf{x}^{(i)})$$

then the prediction becomes

$$f(\mathbf{x}) = \sum_{i=1}^{N} \alpha_i K(\mathbf{x}^{(i)}, \mathbf{x}) + b$$

This means we can build nonlinear predictors from pairwise similarities only.

Common Kernels

Linear Kernel

$$K(\mathbf{x}, \mathbf{x}') = \mathbf{x}^\top \mathbf{x}'$$

This recovers the ordinary linear model.

Polynomial Kernel

$$K(\mathbf{x}, \mathbf{x}') = (\mathbf{x}^\top \mathbf{x}' + c)^p$$

This corresponds to a feature space containing polynomial interactions up to degree $p$.

Radial Basis Function (RBF) Kernel

$$K(\mathbf{x}, \mathbf{x}') = \exp\left(-\frac{\|\mathbf{x} - \mathbf{x}'\|^2}{2\sigma^2}\right)$$

This kernel measures local similarity. Nearby points have kernel value close to 1; distant points have kernel value close to 0.

The bandwidth $\sigma$ controls how local the notion of similarity is:

• small $\sigma$: highly local, flexible, higher variance
• large $\sigma$: smoother, less flexible, higher bias

Prototype Methods vs. Kernel Methods

These two families are related, but they are not identical.

Prototype Methods

• represent data through explicit reference points
• often use distances directly
• can be simple and interpretable
• may store either all examples or only a few summaries

Kernel Methods

• represent similarity implicitly through inner products in feature space
• often support powerful convex optimization formulations
• can produce highly nonlinear decision boundaries
• may become expensive because they compare against many training examples

Prototype methods are usually easier to visualize. Kernel methods are often more mathematically elegant.

Choosing a Distance or Kernel

The method is only as good as its notion of similarity.

Questions to ask:

• Should two inputs be close under Euclidean distance?
• Do I need invariances that raw distance does not capture?
• Are features standardized so that one coordinate does not dominate?
• Is local similarity more important than global linear trends?

Bad features can make a sophisticated kernel method perform worse than a simple linear baseline.

Computational Tradeoffs

Prototype and kernel methods can be expensive when the training set is large.

• k-NN requires comparing a test point with many training examples.
• Kernel methods often require storing many support examples or a Gram matrix.
• Prototype compression helps by reducing the number of reference points.

This creates a familiar tradeoff:

• more stored local structure can improve flexibility
• less stored structure improves speed and memory

When These Methods Shine

They are especially useful when:

• the boundary is not well modeled by a single hyperplane
• local neighborhoods contain meaningful information
• the dataset is medium-sized and similarity is informative
• interpretability through reference examples is helpful

They are weaker when:

• the feature space is poorly scaled
• the dataset is extremely large and latency matters
• similarity in raw input space is not meaningful

You can think of kernels and prototypes as an answer to the limitations of strict linearity:

• GLMs keep the model linear in a transformed output space.
• Kernel and prototype methods keep the prediction rule tied to similarity structure in the input space or feature space.

Both are useful extensions of the basic linear-model toolkit, but they solve different problems.

Recap

1. What is the main idea behind prototype methods?

• Predict using closeness to representative examples
• Replace distances with class probabilities only
• Avoid storing any data after training
• Always fit a single global line

2. What does a kernel function compute?

• A closed-form solution for every classifier
• Only Euclidean distance in the original space
• An inner product in a possibly richer feature space
• The exact generalization error

3. How does decreasing the RBF bandwidth typically change the model?

• It makes similarity more local and the model more flexible
• It guarantees better test accuracy
• It forces the model to become linear
• It removes the need for validation