Online ADMM-Based Extreme Learning Machine for Sparse Supervised Learning

Extreme learning machine based supervised subspace learning

This paper proposes a novel method for supervised subspace learning based on Single-hidden Layer Feedforward Neural networks. The proposed method calculates appropriate network target vectors by formulating a Bayesian model exploiting both the labeling information available for the training data and geometric properties of the training data, when represented in the feature space determined by t...

ADMM Algorithmic Regularization Paths for Sparse Statistical Machine Learning

Optimization approaches based on operator splitting are becoming popular for solving sparsity regularized statistical machine learning models. While many have proposed fast algorithms to solve these problems for a single regularization parameter, conspicuously less attention has been given to computing regularization paths, or solving the optimization problems over the full range of regularizat...

Hessian semi-supervised extreme learning machine

Extreme learning machine (ELM) has emerged as an efficient and effective learning algorithm for classification and regression tasks. Most of the existing research on the ELMs mainly focus on supervised learning. Recently, researchers have extended ELMs for semi-supervised learning, in which they exploit both the labeled and unlabeled data in order to enhance the learning performances. They have...

Visual Tracking Based on Extreme Learning Machine and Sparse Representation

The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly,...

A Robust Online Sequential Extreme Learning Machine