feature extraction – Optimization Online

On the Cluster-aware Supervised Learning (CluSL): Frameworks, Convergent Algorithms, and Applications

Published: 2019/10/29, Updated: 2020/10/20

Data-Mining alternating projection, clustering, feature extraction, globally convergent, regularization, supervised learning

This paper proposes a cluster-aware supervised learning (CluSL) framework, which integrates the clustering analysis with supervised learning (SL). The objective of CluSL is to simultaneously find the best clusters of the data points and minimize the sum of loss functions within each cluster. This framework has many potential applications in healthcare, operations management, manufacturing, and … Read more

A proximal point algorithm for sequential feature extraction applications

Published: 2011/08/03

Xuan Vinh Doan

Kim-Chuan Toh

Stephen A. Vavasis

Data-Mining, Nonsmooth Optimization feature extraction, low-rank optimization, proximal point algorithm

We propose a proximal point algorithm to solve LAROS problem, that is the problem of finding a “large approximately rank-one submatrix”. This LAROS problem is used to sequentially extract features in data. We also develop a new stopping criterion for the proximal point algorithm, which is based on the duality conditions of \eps-optimal solutions of … Read more