Multi-level Feature Learning on Embedding Layer of Convolutional Autoencoders and Deep Inverse Feature Learning for Image Clustering

no code implementations • 5 Oct 2020 • Behzad Ghazanfari, Fatemeh Afghah

This paper introduces Multi-Level feature learning alongside the Embedding layer of Convolutional Autoencoder (CAE-MLE) as a novel approach in deep clustering.

Clustering Deep Clustering +1

Piece-wise Matching Layer in Representation Learning for ECG Classification

no code implementations • 26 Sep 2020 • Behzad Ghazanfari, Fatemeh Afghah, Sixian Zhang

To evaluate the performance of this method in time series analysis, we applied the proposed layer in two publicly available datasets of PhysioNet competitions in 2015 and 2017 where the input data is ECG signal.

Classification ECG Classification +4

Deep Inverse Feature Learning: A Representation Learning of Error

no code implementations • 9 Mar 2020 • Behzad Ghazanfari, Fatemeh Afghah

This paper introduces a novel perspective about error in machine learning and proposes inverse feature learning (IFL) as a representation learning approach that learns a set of high-level features based on the representation of error for classification or clustering purposes.

Classification Clustering +3

Inverse Feature Learning: Feature learning based on Representation Learning of Error

no code implementations • 8 Mar 2020 • Behzad Ghazanfari, Fatemeh Afghah, Mohammadtaghi Hajiaghayi

This paper proposes inverse feature learning as a novel supervised feature learning technique that learns a set of high-level features for classification based on an error representation approach.

General Classification Representation Learning

An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs

no code implementations • 17 Apr 2019 • Behzad Ghazanfari, Fatemeh Afghah, Kayvan Najarian, Sajad Mousavi, Jonathan Gryak, James Todd

In this paper, we propose a novel set of high-level features based on unsupervised feature learning technique in order to effectively capture the characteristics of different arrhythmia in electrocardiogram (ECG) signal and differentiate them from irregularity in signals due to different sources of signal disturbances.

Clustering Specificity

Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach

no code implementations • 17 Nov 2018 • Behzad Ghazanfari, Fatemeh Afghah, Matthew E. Taylor

Reinforcement learning (RL) techniques, while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces.

reinforcement-learning Reinforcement Learning (RL) +1

Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning

no code implementations • 14 Sep 2017 • Behzad Ghazanfari, Matthew E. Taylor

This paper proposes a novel practical method that can autonomously decompose tasks, by leveraging association rule mining, which discovers hidden relationship among entities in data mining.

reinforcement-learning Reinforcement Learning (RL)