TY - GEN

T1 - Improved Predictor-Corrector Algorithm

AU - Pazira, Hassan

PY - 2020

Y1 - 2020

N2 - The differential geometric least angle regression method consists essentially in computing the solution path. In Augugliaro et al. [4], this problem is satisfactorily solved by using a predictor-corrector (PC) algorithm, that however has the drawback of becoming intractable when working with thousands of predictors. Using the PC algorithm leads to an increase in the run times needed for computing the solution curve. In this paper we explain an improved version of the PC algorithm (IPC), proposed in Pazira et al. [9], to decrease the effects stemming from this problem for computing the solution curve. The IPC algorithm allows the dgLARS method to be implemented by using less number of arithmetic operations that leads to potential computational saving.

AB - The differential geometric least angle regression method consists essentially in computing the solution path. In Augugliaro et al. [4], this problem is satisfactorily solved by using a predictor-corrector (PC) algorithm, that however has the drawback of becoming intractable when working with thousands of predictors. Using the PC algorithm leads to an increase in the run times needed for computing the solution curve. In this paper we explain an improved version of the PC algorithm (IPC), proposed in Pazira et al. [9], to decrease the effects stemming from this problem for computing the solution curve. The IPC algorithm allows the dgLARS method to be implemented by using less number of arithmetic operations that leads to potential computational saving.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85082124372&origin=inward

U2 - 10.1007/978-3-030-34585-3_9

DO - 10.1007/978-3-030-34585-3_9

M3 - Conference contribution

SN - 9783030345846

VL - 11925 LNBI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 99

EP - 106

BT - Computational Intelligence Methods for Bioinformatics and Biostatistics - 15th International Meeting, CIBB 2018, Revised Selected Papers

A2 - Raposo, Maria

A2 - Sério, Susana

A2 - Staiano, Antonino

A2 - Ciaramella, Angelo

PB - Springer

T2 - 15th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2018

Y2 - 6 September 2018 through 8 September 2018

ER -