Uploaded byMasayuki Tanaka
Lasso regression
This document derives the closed-form soft threshold solution for Lasso regression. It begins by defining the cost function for Lasso regression and orthonormal Lasso regression. It then shows the derivation step-by-step, considering the cost function element-wise. There are three cases: when the ordinary least squares estimate is less than the threshold, equal to the threshold, and greater than the threshold. In each case, the soft threshold solution is defined. The final solution is expressed compactly as the sign of the OLS estimate times the soft-thresholded value.
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Lasso regression
- 1. Masayuki Tanaka Jun. 17,2016 Derivation of the closed soft threshold solution of the Lasso regression
- 2. Lasso regression The costfunction of Lasso regression: 𝐿 𝜷, 𝜆 = 1 2 𝒀 − 𝑿𝜷 2 2 + 𝜆 𝜷 1 Y:Data matrix X:System matrix
- 3. Orthonormal Lasso regression 𝐿𝜷, 𝜆 = 1 2 𝒀 − 𝑿𝜷 2 2 + 𝜆 𝜷 1 where 𝑿 𝑇 𝑿 = 𝑰 (orthonormal)The closed form soft threshold solution 𝛽𝑗 = sign 𝛽𝑗 OLS 𝛽𝑗 OLS − 𝜆 + 𝜷OLS = arg min 𝜷 1 2 𝒀 − 𝑿𝜷 2 2 = 𝑿 𝑇 𝑿 −1 𝑿 𝑇 𝒀 = 𝑿 𝑇 𝒀 sign 𝜉 = −1 (𝜉 < 0) 0 (𝜉 = 0) 1 (𝜉 > 0) 𝜉 + = max 𝜉, 0 = 𝜉 (𝜉 > 0) 0 (𝜉 ≤ 0)
- 4. Derivation of thesoft threshold solution arg min 𝜷 1 2 𝒀 − 𝑿𝜷 2 2 + 𝜆 𝜷 1 = arg min 𝜷 1 2 𝒀 𝑇 𝒀 − 2𝜷 𝑇 𝑿 𝑻 𝒀 + 𝜷 𝑇 𝑿 𝑻 𝑿𝜷 + 𝜆 𝜷 1 = arg min 𝜷 1 2 −2𝜷 𝑇 𝜷OLS + 𝜷 𝑇 𝜷 + 𝜆 𝜷 1 𝒀 𝑇 𝒀 = 𝒄𝒐𝒏𝒔𝒕 𝑿 𝑻 𝒀 = 𝜷OLS 𝑿 𝑻 𝑿 = 𝑰 (We can consider element-wise) arg min 𝛽𝑗 𝐶 𝛽𝑗 = arg min 𝛽𝑗 1 2 𝛽𝑗 2 − 𝛽𝑗 OLS 𝛽𝑗 + 𝜆 𝛽𝑗 𝛽𝑗 = 0 𝛽𝑗 = 0 𝛽𝑗 > 0 𝐶 𝛽𝑗 = 1 2 𝛽𝑗 2 − 𝛽𝑗 OLS 𝛽𝑗 + 𝜆𝛽𝑗 = 𝛽𝑗 1 2 𝛽𝑗 − 𝛽𝑗 OLS + 𝜆 𝛽𝑗 = 𝛽𝑗 OLS − 𝜆 𝛽𝑗 < 0 𝐶 𝛽𝑗 = 1 2 𝛽𝑗 2 − 𝛽𝑗 OLS 𝛽𝑗 − 𝜆𝛽𝑗 = 𝛽𝑗 1 2 𝛽𝑗 − 𝛽𝑗 OLS − 𝜆 𝛽𝑗 = 𝛽𝑗 OLS + 𝜆
- 5. Derivation of thesoft threshold solution 𝛽𝑗 OLS −𝜆 𝜆 Case: 𝛽𝑗 OLS < −𝜆 𝛽𝑗 OLS − 𝜆 < 0𝛽𝑗 OLS + 𝜆 < 0 𝛽𝑗 𝐶 𝛽𝑗 𝛽𝑗 = 𝛽𝑗 OLS + 𝜆 𝛽𝑗 OLS −𝜆 𝜆 Case: −𝜆 ≤ 𝛽𝑗 OLS ≤ 𝜆 𝛽𝑗 OLS − 𝜆 ≤ 0𝛽𝑗 OLS + 𝜆 ≥ 0 𝛽𝑗 𝐶 𝛽𝑗 𝛽𝑗 = 0 𝛽𝑗 OLS −𝜆 𝜆 Case: 𝜆 < 𝛽𝑗 OLS 𝛽𝑗 OLS − 𝜆 > 0𝛽𝑗 OLS + 𝜆 > 0 𝛽𝑗 𝐶 𝛽𝑗 𝛽𝑗 = 𝛽𝑗 OLS − 𝜆
- 6. Derivation of thesoft threshold solution Case: 𝛽𝑗 OLS < −𝜆, 𝛽𝑗 = 𝛽𝑗 OLS + 𝜆 Case: −𝜆 ≤ 𝛽𝑗 OLS ≤ 𝜆,𝛽𝑗 = 0 Case: 𝜆 < 𝛽𝑗 OLS , 𝛽𝑗 = 𝛽𝑗 OLS − 𝜆 𝛽𝑗 = sign 𝛽𝑗 OLS 𝛽𝑗 OLS − 𝜆 + 𝛽𝑗 = sign 𝛽𝑗 OLS 𝛽𝑗 OLS − 𝜆 + = −1 − 𝛽𝑗 OLS − 𝜆 + = 𝛽𝑗 OLS + 𝜆 𝛽𝑗 = sign 𝛽𝑗 OLS 𝛽𝑗 OLS − 𝜆 + = sign 𝛽𝑗 OLS × 0 = 0 𝛽𝑗 = sign 𝛽𝑗 OLS 𝛽𝑗 OLS − 𝜆 + = +1 𝛽𝑗 OLS − 𝜆 + = 𝛽𝑗 OLS − 𝜆
- 7. Reference High-dimensional data analysis,Lecture 6 (Lasso Regression) by Wessel van Wierin