Lasso Regression (L1 Regularization)

In the previous module, we explored Ridge Regression—the elegant solution that tames overfitting by adding an L2 penalty that shrinks all coefficients toward zero. Ridge regression is remarkably effective: it stabilizes ill-conditioned problems, handles multicollinearity gracefully, and provides a smooth bias-variance tradeoff.

But ridge regression has a fundamental limitation: it never produces exactly zero coefficients. Every feature, no matter how irrelevant, retains some non-zero weight in the final model. When you have 1,000 potential features and suspect only 20 truly matter, ridge gives you a model with 1,000 non-zero coefficients—making interpretation difficult and suggesting false relationships.

This is where Lasso regression (Least Absolute Shrinkage and Selection Operator) enters the picture. Proposed by Robert Tibshirani in 1996, Lasso makes a seemingly small modification to the regularization term: replace the squared L2 penalty with an absolute value L1 penalty. This single change produces dramatically different behavior—Lasso doesn't just shrink coefficients; it eliminates them entirely.

Let us begin by precisely defining the Lasso objective. Consider the standard linear regression setting:

• We have $n$ observations and $p$ features
• Design matrix $\mathbf{X} \in \mathbb{R}^{n \times p}$
• Response vector $\mathbf{y} \in \mathbb{R}^n$
• Coefficient vector $\boldsymbol{\beta} \in \mathbb{R}^p$

The Lasso Objective Function:

$$\hat{\boldsymbol{\beta}}^{\text{lasso}} = \underset{\boldsymbol{\beta}}{\arg\min} \left{ \frac{1}{2n} |\mathbf{y} - \mathbf{X}\boldsymbol{\beta}|_2^2 + \lambda |\boldsymbol{\beta}|_1 \right}$$

Let's dissect each component of this formulation:

The L1 norm (also called the Manhattan norm, taxicab norm, or ℓ₁ norm) is a fundamental concept in linear algebra and analysis. For a vector $\mathbf{v} = (v_1, v_2, \ldots, v_p)^T$, the L1 norm is defined as:

$$|\mathbf{v}|1 = \sum{j=1}^{p} |v_j| = |v_1| + |v_2| + \cdots + |v_p|$$

The name "Manhattan distance" comes from the grid-like street layout of Manhattan: to travel between two intersections, you must walk along streets (horizontal and vertical), not diagonally through buildings.

Properties of the L1 Norm:

The Subdifferential at Zero:

The non-differentiability of $|v|$ at $v = 0$ is not a bug—it's the feature that enables sparsity. Let's examine this carefully using the concept of subdifferentials from convex analysis.

For a convex function $f$, the subdifferential at a point $x$ is the set of all slopes of lines that lie entirely below the graph of $f$ while touching it at $x$:

$$\partial f(x) = {g : f(y) \geq f(x) + g(y - x) \text{ for all } y}$$

For the absolute value function $f(v) = |v|$:

$$\partial |v| = \begin{cases} {1} & \text{if } v > 0 \ {-1} & \text{if } v < 0 \ [-1, 1] & \text{if } v = 0 \end{cases}$$

This interval $[-1, 1]$ at zero is crucial. It means that any subgradient between $-1$ and $1$ is valid at the origin. This "slack" allows the optimization to settle at exactly zero when the gradient of the data term is small enough.

To appreciate the Lasso's unique behavior, we must contrast it mathematically with ridge regression. Both methods add a penalty to ordinary least squares, but the nature of that penalty changes everything.

Side-by-Side Comparison:

The Penalty Behavior Near Zero:

The fundamental difference emerges when we examine how each penalty behaves for small coefficient values.

Consider moving a coefficient from $\beta_j = 0.01$ to $\beta_j = 0$:

• L2 Penalty Change: $(0.01)^2 - 0^2 = 0.0001$ — a tiny reduction
• L1 Penalty Change: $|0.01| - |0| = 0.01$ — a much larger reduction (100× more)

This asymmetry is profound. The L1 penalty gives the same "credit" for reducing $|\beta_j|$ from $0.01$ to $0$ as it does for reducing from $100.01$ to $100$. In contrast, L2 provides nearly zero incentive to shrink already-small coefficients to exactly zero.

Mathematically:

• For L2: $\frac{d}{d\beta_j}(\beta_j^2) = 2\beta_j \to 0$ as $\beta_j \to 0$
• For L1: $\frac{d}{d\beta_j}|\beta_j| = \text{sign}(\beta_j) = \pm 1$ (constant magnitude)

The derivative of the L1 penalty doesn't diminish as coefficients approach zero—it maintains constant pressure to drive coefficients all the way to zero.

The Lasso can be equivalently expressed as a constrained optimization problem. This formulation provides complementary geometric intuition and connects to the broader theory of regularization.

Constrained Form:

$$\hat{\boldsymbol{\beta}}^{\text{lasso}} = \underset{\boldsymbol{\beta}}{\arg\min} \left{ \frac{1}{2n}|\mathbf{y} - \mathbf{X}\boldsymbol{\beta}|_2^2 \right} \quad \text{subject to} \quad |\boldsymbol{\beta}|_1 \leq t$$

Equivalence via Lagrange Duality:

For every value of the regularization parameter $\lambda \geq 0$, there exists a corresponding constraint bound $t \geq 0$ such that the solutions are identical. The relationship is governed by Lagrange duality:

• The penalized form uses $\lambda$ as a Lagrange multiplier
• The constrained form uses $t$ as an explicit bound
• Strong duality holds because both the objective and constraint set are convex

Interpreting the Constraint $|\boldsymbol{\beta}|_1 \leq t$:

This constraint defines an L1 ball in coefficient space—the set of all coefficient vectors whose L1 norm is at most $t$. The geometric shape of this ball is crucial:

Why Corners Matter for Sparsity:

Optimization seeks to minimize the loss function while staying within the constraint set. Geometrically, we expand contour ellipses of the loss until they just touch the constraint boundary.

For L2 constraints (a sphere), the touching point can occur anywhere on the smooth boundary. For L1 constraints (a diamond), the touching point tends to occur at corners—and corners are where coordinates equal zero.

This is the geometric revelation: the sharp corners of the L1 ball "catch" the expanding loss contours, naturally producing solutions on coordinate axes (sparse solutions).

While Lasso lacks a closed-form solution in general, there is one special case where the solution has a beautiful explicit form: orthonormal design matrices.

Setup: Orthonormal Design

Assume $\mathbf{X}$ has orthonormal columns: $\mathbf{X}^T\mathbf{X} = \mathbf{I}$. This is achievable through QR decomposition or when features are uncorrelated and standardized.

Let $\tilde{\boldsymbol{\beta}}^{\text{OLS}} = \mathbf{X}^T\mathbf{y}$ be the OLS solution (which equals $(\mathbf{X}^T\mathbf{X})^{-1}\mathbf{X}^T\mathbf{y} = \mathbf{X}^T\mathbf{y}$ under orthonormality).

The Soft Thresholding Operator:

For orthonormal $\mathbf{X}$, the Lasso solution is:

$$\hat{\beta}j^{\text{lasso}} = S{\lambda}(\tilde{\beta}_j^{\text{OLS}}) = \text{sign}(\tilde{\beta}_j^{\text{OLS}}) \cdot \max(|\tilde{\beta}_j^{\text{OLS}}| - \lambda, 0)$$

This is the soft thresholding function (also called the shrinkage operator), often denoted $S_\lambda(z)$.

Properties of Soft Thresholding:

1. Sparsity Zone: If $|\tilde{\beta}_j^{\text{OLS}}| \leq \lambda$, then $\hat{\beta}_j^{\text{lasso}} = 0$ (coefficient is eliminated)

2. Uniform Shrinkage: If $|\tilde{\beta}_j^{\text{OLS}}| > \lambda$, the coefficient is shrunk by exactly $\lambda$ in absolute value

3. Continuity: Unlike hard thresholding (which jumps from zero to non-zero), soft thresholding is continuous

4. Bias: Large coefficients are shrunk by $\lambda$, introducing bias for truly important features

Comparison: Soft vs. Hard vs. Ridge

Since the L1 norm is not differentiable everywhere, we cannot simply set the gradient to zero. Instead, we use the Karush-Kuhn-Tucker (KKT) conditions from convex optimization, which generalize first-order optimality conditions to non-smooth functions.

The Lasso Optimality Conditions:

A vector $\hat{\boldsymbol{\beta}}$ is optimal for the Lasso problem if and only if:

$$\frac{1}{n}\mathbf{X}^T(\mathbf{X}\hat{\boldsymbol{\beta}} - \mathbf{y}) + \lambda \hat{\mathbf{s}} = \mathbf{0}$$

where $\hat{\mathbf{s}} \in \partial|\hat{\boldsymbol{\beta}}|_1$ is a subgradient of the L1 norm at $\hat{\boldsymbol{\beta}}$.

Subgradient Components:

For each coordinate $j$:

$$\hat{s}_j \in \partial|\hat{\beta}_j| = \begin{cases} {\text{sign}(\hat{\beta}_j)} & \text{if } \hat{\beta}_j \neq 0 \ [-1, 1] & \text{if } \hat{\beta}_j = 0 \end{cases}$$

Coordinate-wise KKT Conditions:

Let $\mathbf{r} = \mathbf{y} - \mathbf{X}\hat{\boldsymbol{\beta}}$ be the residual vector. The optimality conditions per coordinate are:

Interpretation:

These conditions reveal the fundamental mechanism of Lasso sparsity:

1. Active Features ($\hat{\beta}_j \neq 0$): Must have residual correlations of exactly $\pm\lambda$. They are "maxed out" in their contribution.

2. Inactive Features ($\hat{\beta}_j = 0$): Have residual correlations strictly less than $\lambda$ in magnitude. They "can't compete" with the active features.

The regularization parameter $\lambda$ acts as a threshold: only features sufficiently correlated with the residual (after accounting for other features) earn non-zero coefficients.

Finding the Critical $\lambda$:

At what $\lambda$ does a coefficient first become non-zero? When $\lambda$ is very large, all coefficients are zero. As we decrease $\lambda$, coefficients enter the model one by one.

The first feature to enter is the one most correlated with the response:

$$\lambda_{\max} = \frac{1}{n}|\mathbf{X}^T\mathbf{y}|_\infty = \max_j \left|\frac{1}{n}\mathbf{x}_j^T\mathbf{y}\right|$$

For $\lambda \geq \lambda_{\max}$, the solution is $\hat{\boldsymbol{\beta}} = \mathbf{0}$.

Proper application of Lasso requires careful attention to standardization and intercept handling. Neglecting these considerations leads to models that penalize features unequally based on arbitrary scaling choices.

Why Standardization Matters:

The L1 penalty $\lambda\sum_j|\beta_j|$ treats all coefficients equally. But if features have different scales, this equality is misleading:

• Feature $A$ measured in millimeters has coefficient $\beta_A = 1000$
• Feature $B$ measured in meters has coefficient $\beta_B = 1$
• Both encode the same relationship, but $|\beta_A|$ is 1000× larger

Without standardization, the Lasso would heavily penalize feature $A$ simply because of measurement units.

Standard Preprocessing:

Handling the Intercept:

The intercept $\beta_0$ should generally not be penalized. Penalizing the intercept shifts predictions away from the data's natural center, introducing unnecessary bias.

The standard approach:

1. Center $\mathbf{y}$ and columns of $\mathbf{X}$
2. Fit the Lasso on centered data (intercept is implicitly zero)
3. Recover the original-scale intercept: $\hat{\beta}_0 = \bar{y} - \sum_j \hat{\beta}_j \bar{x}_j$

Mathematical Justification:

With centered data, the gradient of the RSS with respect to $\beta_0$ is:

$$\frac{\partial}{\partial \beta_0} \sum_i (y_i - \beta_0 - \mathbf{x}_i^T\boldsymbol{\beta})^2 = -2\sum_i(y_i - \beta_0 - \mathbf{x}_i^T\boldsymbol{\beta})$$

Setting to zero: $\hat{\beta}_0 = \bar{y} - \bar{\mathbf{x}}^T\hat{\boldsymbol{\beta}}$

When $\bar{y} = 0$ and $\bar{\mathbf{x}} = \mathbf{0}$ (centered data), this gives $\hat{\beta}_0 = 0$.

We have established the mathematical foundation of Lasso regression. Let's consolidate the key insights before moving to the geometric interpretation in the next page.

What's Next:

We've established why the L1 penalty produces different behavior than L2, but we haven't yet visualized how sparsity emerges geometrically. The next page provides the crucial geometric interpretation—showing how the diamond-shaped L1 constraint set interacts with elliptical level sets to produce sparse solutions at corners.