Robust Ridge Regression for High-Dimensional Data

Technometrics ◽  
2011 ◽  
Vol 53 (1) ◽  
pp. 44-53 ◽  
Author(s):  
Ricardo A. Maronna
Keyword(s):  
Ridge Regression ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals ANALISIS TINGKAT KEMISKINAN DI PROVINSI SUMATERA BARAT MELALUI PENDEKATAN REGRESI TERKENDALA (RIDGE REGRESSION, LASSO, DAN ELASTIC NET)

2021 ◽  
Vol 21 (1) ◽  
pp. 29-36
Author(s):  
Fitri Mudia Sari ◽  
Khairil Anwar Notodiputro ◽  
Bagus Sartono
Keyword(s):  
Ridge Regression ◽  
High Dimensional Data ◽  
Elastic Net ◽  
High Dimensional

Pandemi Covid-19 yang mulai menyerang Indonesia semenjak Maret 2020 menyebabkan krisis ekonomi dan sosial di Indonesia, termasuk Sumatera Barat. Data BPS Sumatera Barat menyebutkan bahwa jumlah penduduk miskin bertambah sebanyak 20.056, dari 344.023 orang pada Maret 2020, menjadi 364.079 pada September 2020. Masalah kemiskinan merujuk pada konsep high dimensional data yang melibatkan banyak peubah sehingga digunakan Regresi Ridge, LASSO, dan Elastic Net yang dapat mengatasi masalah multikolinieritas. Penelitian ini bertujuan untuk melihat peubah yang memiliki pengaruh yang penting terhadap tingkat kemiskinan di Sumatera Barat menggunakan model terbaik yang terpilih dari Regresi Ridge, LASSO, dan Elastic Net. Hasil penelitian menunjukkan bahwa tingkat buta huruf merupakan peubah penting yang mempengaruhi tingkat kemiskinan di Sumatera Barat dengan model terbaik yaitu Regresi Ridge.

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional

Approximate Cluster Heat Maps of Large High-Dimensional Data

2018 ◽  
Author(s):  
Punit Rathore ◽  
James C. Bezdek ◽  
Dheeraj Kumar ◽  
Sutharshan Rajasegarar ◽  
Marimuthu Palaniswami
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Heat Maps

scholarly journals The Generalized Bayes Method for High-Dimensional Data Recognition with Applications to Audio Signal Recognition

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 19
Author(s):  
Hsiuying Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Conventional Method ◽  
High Dimensional Data ◽  
Audio Signal ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Signal Recognition ◽  
Bayes Method ◽  
Generalized Bayes

High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application.

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