Lasso

Dec 3, 20170 likes208 views

This document discusses Lasso and Bayesian Lasso regression. It provides an overview of Lasso, which performs variable selection by shrinking coefficients towards zero. Bayesian Lasso regression places Laplace priors on the coefficients, allowing posterior inference using Gibbs sampling. The document demonstrates an example of Bayesian Lasso for diabetes data and prostate cancer data, comparing errors to non-Bayesian Lasso and examining coefficient estimates.

Lasso and Bayesian Lasso
Liangqun Lu @Bayesian Inference 2017 Spring
Biology Department
University of Memphis

Outline
? Lasso
? Bayesian Lasso Regression
? Application Example

LASSO
? Least Absolute Shrinkage and Selection Operator
? Usage: Variable Selection
�� Variables are greater than observations (p > n)
�� Shrinkage = loss function + penalty function

Lasso (Tibshirani, 1996)
Courtesy http://www.stat.wisc.edu/~shao/stat992/pres1-1.pdf

The loss function || y - X��||2
= (�� ? ��) T X T X(�� ? ��) + constant
Like when p = 2
|��1
| + |��2
| �� t
Some �� are set to 0
Lasso solution is the touching place
?? ( 2 )

Bayesian Lasso Regression (CHRIS HAN, 2009)

Diabetes example
Training:
192 X 8
Testing:
250 X 8
? Compare the error between non- bayes and bayes
? T = 1000 times
? orthogonalized Gibbs sampler
? Calculate the posterior probability
(The sampling results can be seen from R monomvn package)

Prostate example
(97 X 9)
Outcome: cancer volume
Variables: prostate weight; age; benign prostatic hyperplasia amount; seminal vesicle
invasion; capsular penetration; Gleason score; percentage Gleason scores 4 or 5; prostate
specific antigen

Coefficients from Lasso regularization
Optimization from Cross Validation
�� = 0.01899013
Log�� = -3.963836
> coef(cvfit, s = "lambda.min")
(Intercept) -1.642167273
lweight -0.009419681
age 0.016850891
lbph -0.066278770
svi .
lcp 0.317658663
gleason 0.116595770
pgg45 -0.003493425
lpsa 0.529322761

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At the BIOSTEC 2025 conference, Eleni Kaldoudi, ThrombUS+ project coordinator, presented our recent work entitled ��Preparing Ultrasound Imaging Data for Artificial Intelligence Tasks: Anonymisation, Cropping, and Tagging��. Eleni provided an overview of the application we developed to facilitate the preparation of ultrasound images, acquired via the ThrombUS+ clinical study A, for the purpose of developing AI models for automated detection of deep vein thrombosis. About ThrombUS+: Our interdisciplinary approach centers around creating a novel wearable diagnostic device utilizing autonomous, AI-driven DVT detection. This groundbreaking device incorporates wearable ultrasound hardware, impedance plethysmography, and light reflection rheography for early clot detection. Activity and physiological measurements will continuously assess DVT risk, supporting prevention through serious gaming. An intelligent decision support unit will provide real-time monitoring and alerts, with extended reality guiding users for optimal device utilization. ThrombUS+ is designed for postoperative patients, those undergoing lengthy surgical procedures, cancer patients, bedridden individuals at home or in care units, and women during pregnancy and postpartum.

(Chapter Summary) Molecular Biology of the Cell, Chapter 18: Cell DeathDavid Podorefsky, PhD

(Journal Club) Focused ultrasound excites neurons via mechanosensitive calciu...David Podorefsky, PhD

Parasitology Practical Book .pdf Biomedical sciencesaihetharan

Hormones and the Endocrine System | IGCSE BiologyBlessing Ndazie

This IGCSE Biology presentation explores hormones and the endocrine system, explaining their role in controlling body functions. Learn about the differences between nervous and hormonal control, major endocrine glands, key hormones (such as insulin, adrenaline, and testosterone), and homeostasis. Understand how hormones regulate growth, metabolism, reproduction, and the fight-or-flight response. A perfect resource for Cambridge IGCSE students preparing for exams!

(Journal Club) Folding DNA to create nanoscale shapes and patternsDavid Podorefsky, PhD

B-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM PruningVasileiosMezaris

Drug evaluation�C Organoleptic, Microscopic, Chemical, BiologicalNistarini College, Purulia (W.B) India