scholarly journals Online Learning from Capricious Data Streams: A Generative Approach

2019 ◽  
Author(s):  
Yi He ◽  
Baijun Wu ◽  
Di Wu ◽  
Ege Beyazit ◽  
Sheng Chen ◽  
...  
Keyword(s):  
Online Learning ◽  
Data Streams ◽  
Graphical Model ◽  
Feature Space ◽  
Streaming Data ◽  
The Past ◽  
Universal Feature ◽  
New Feature ◽  
Space Dynamics ◽  
Generative Approach

Learning with streaming data has received extensive attention during the past few years. Existing approaches assume the feature space is fixed or changes by following explicit regularities, limiting their applicability in dynamic environments where the data streams are described by an arbitrarily varying feature space. To handle such capricious data streams, we in this paper develop a novel algorithm, named OCDS (Online learning from Capricious Data Streams), which does not make any assumption on feature space dynamics. OCDS trains a learner on a universal feature space that establishes relationships between old and new features, so that the patterns learned in the old feature space can be used in the new feature space. Specifically, the universal feature space is constructed by leveraging the relatednesses among features. We propose a generative graphical model to model the construction process, and show that learning from the universal feature space can effectively improve performance with theoretical analysis. The experimental results demonstrate that OCDS achieves conspicuous performance on synthetic and real datasets.

scholarly journals Online Learning from Data Streams with Varying Feature Spaces

2019 ◽  
Vol 33 ◽  
pp. 3232-3239 ◽  
Author(s):  
Ege Beyazit ◽  
Jeevithan Alagurajah ◽  
Xindong Wu
Keyword(s):  
Online Learning ◽  
Data Streams ◽  
Learning Algorithm ◽  
Feature Space ◽  
Model Complexity ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Empirical Risk ◽  
Feature Spaces ◽  
Online Learning Algorithm

We study the problem of online learning with varying feature spaces. The problem is challenging because, unlike traditional online learning problems, varying feature spaces can introduce new features or stop having some features without following a pattern. Other existing methods such as online streaming feature selection (Wu et al. 2013), online learning from trapezoidal data streams (Zhang et al. 2016), and learning with feature evolvable streams (Hou, Zhang, and Zhou 2017) are not capable to learn from arbitrarily varying feature spaces because they make assumptions about the feature space dynamics. In this paper, we propose a novel online learning algorithm OLVF to learn from data with arbitrarily varying feature spaces. The OLVF algorithm learns to classify the feature spaces and the instances from feature spaces simultaneously. To classify an instance, the algorithm dynamically projects the instance classifier and the training instance onto their shared feature subspace. The feature space classifier predicts the projection confidences for a given feature space. The instance classifier will be updated by following the empirical risk minimization principle and the strength of the constraints will be scaled by the projection confidences. Afterwards, a feature sparsity method is applied to reduce the model complexity. Experiments on 10 datasets with varying feature spaces have been conducted to demonstrate the performance of the proposed OLVF algorithm. Moreover, experiments with trapezoidal data streams on the same datasets have been conducted to show that OLVF performs better than the state-of-the-art learning algorithm (Zhang et al. 2016).

scholarly journals EvolveCluster: an evolutionary clustering algorithm for streaming data

2021 ◽  
Author(s):  
Christian Nordahl ◽  
Veselka Boeva ◽  
Håkan Grahn ◽  
Marie Persson Netz
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Streaming Data ◽  
Evolutionary Clustering ◽  
Stream Clustering ◽  
The Past ◽  
Data Stream Clustering ◽  
Evolving Data

AbstractData has become an integral part of our society in the past years, arriving faster and in larger quantities than before. Traditional clustering algorithms rely on the availability of entire datasets to model them correctly and efficiently. Such requirements are not possible in the data stream clustering scenario, where data arrives and needs to be analyzed continuously. This paper proposes a novel evolutionary clustering algorithm, entitled EvolveCluster, capable of modeling evolving data streams. We compare EvolveCluster against two other evolutionary clustering algorithms, PivotBiCluster and Split-Merge Evolutionary Clustering, by conducting experiments on three different datasets. Furthermore, we perform additional experiments on EvolveCluster to further evaluate its capabilities on clustering evolving data streams. Our results show that EvolveCluster manages to capture evolving data stream behaviors and adapts accordingly.

Genetic variant effect prediction by supervised nonnegative matrix tri-factorization

2021 ◽  
Author(s):  
Asieh Amousoltani Arani ◽  
Mohammadreza Sehhati ◽  
Mohammad Amin Tabatabaiefar
Keyword(s):  
Input Data ◽  
Genetic Variant ◽  
Nonnegative Matrix ◽  
Feature Space ◽  
Variant Effect ◽  
New Feature ◽  
Variant Effect Prediction

A new feature space, which can discriminate deleterious variants, was constructed by the integration of various input data using the proposed supervised nonnegative matrix tri-factorization (sNMTF) algorithm.

METHOD FOR ANALYSIS OF THE DOPPLER-BROADENED SPECTRA OF LASER RADIATION FOR THE ASSESSMENT OF BLOOD MICROCIRCULATION DISTURBANCES IN DIABETES MELLITUS TYPE 2

2020 ◽  
Vol 4 (1) ◽  
pp. 80-87
Author(s):  
I.O. KOZLOV
Keyword(s):  
Diabetes Mellitus ◽  
Laser Radiation ◽  
Laser Doppler Flowmetry ◽  
Diabetes Mellitus Type 2 ◽  
Feature Space ◽  
Diabetes Mellitus Type ◽  
Laser Doppler ◽  
Doppler Flowmetry ◽  
New Feature

The article is devoted to the development of laser Doppler flowmetry and analysis of the recorded signal to study the distribution of perfusion over the frequencies of Doppler broadening of laser radiation. The processing algorithm and the necessary technical conditions for the correct registration of the signal are shown. As examples of the proposed method implementation, the data are obtained from a healthy volunteer and a patient with diabetes mellitus type 2 and analyzed. According to the proposed method, processing of recorded data provides a new feature space for data analysis of laser Doppler flowmetry signal.

An FPGA-based SOM circuit architecture for online learning of 64-QAM data streams

2018 ◽  
Author(s):  
Miguel Angelo de Abreu de Sousa ◽  
Ricardo Pires ◽  
Sara D. dos S. Perseghini ◽  
Emilio Del-Moral-Hernandez
Keyword(s):  
Online Learning ◽  
Data Streams

scholarly journals Survey on Feature Transformation Techniques for Data Streams

2020 ◽  
Author(s):  
Maroua Bahri ◽  
Albert Bifet ◽  
Silviu Maniu ◽  
Heitor Murilo Gomes
Keyword(s):  
Machine Learning ◽  
Data Streams ◽  
Large Data ◽  
High Dimensional ◽  
Feature Transformation ◽  
Transformation Techniques ◽  
Computational Costs ◽  
The Past ◽  
Fundamental Challenge ◽  
Mining Algorithms

Mining high-dimensional data streams poses a fundamental challenge to machine learning as the presence of high numbers of attributes can remarkably degrade any mining task's performance. In the past several years, dimension reduction (DR) approaches have been successfully applied for different purposes (e.g., visualization). Due to their high-computational costs and numerous passes over large data, these approaches pose a hindrance when processing infinite data streams that are potentially high-dimensional. The latter increases the resource-usage of algorithms that could suffer from the curse of dimensionality. To cope with these issues, some techniques for incremental DR have been proposed. In this paper, we provide a survey on reduction approaches designed to handle data streams and highlight the key benefits of using these approaches for stream mining algorithms.

scholarly journals E-learning rhetoric, creativity and innovation

2005 ◽  
Vol 13 (2) ◽  
Author(s):  
Jane Seale
Keyword(s):  
Online Learning ◽  
Policy Evaluation ◽  
Learning Technology ◽  
Current Position ◽  
The Past ◽  
E Learning ◽  
Creativity And Innovation

In this issue of ALT-J we have five papers that cover a range of policy, evaluation and development issues. The first paper, by Smith, sets the scene for the remaining papers with its focus on policy and how this may be influenced by rhetoric, and in turn may influence creativity and innovation. In ‘From flowers to palms: 40 years of policy for online learning’, Smith presents a review of learning technology-related policy over the past 40 years. The purpose of the review is to make sense of the current position in which the field finds itself, and to highlight lessons that can be learned from the implementation of previous policies.DOI: 10.1080/09687760500104039

Impact of Covid-19 on the Use of Laptops by College Students and the Effects on Posture and Discomfort

2021 ◽  
Vol 65 (1) ◽  
pp. 705-707
Author(s):  
Susan E. Kotowski ◽  
Kermit G. Davis
Keyword(s):  
College Students ◽  
Online Learning ◽  
Technology Use ◽  
Education And Training ◽  
Upper Extremities ◽  
Work Environments ◽  
Good Work ◽  
The Past ◽  
And Training

Over the past year, the Covid-19 pandemic has led to a switch from a majority of classes being taught in-person to a majority being taught online. The switch has led to an increase in the amount of time students are utilizing technology for learning purposes. This study assessed how technology use has changed during the pandemic, particularly related to laptop use, and the postures students work in and the discomfort they’re experiencing while participating in online learning. The results of the survey (n=1,074) found that laptop use is up significantly (used the majority of the time by 70.2% of students), students are working in poor postures (up to 80% working with deviated neck postures), and are experiencing high levels of discomfort (up to ~60% reporting moderate/extreme discomfort in their upper extremities). The results bring to light the urgent need to provide ergonomics education and training for designing good work environments.

Applying Constructivism to Online Learning

2009 ◽  
pp. 58-73 ◽  
Author(s):  
Jennifer Lee ◽  
Lin Lin
Keyword(s):  
Online Learning ◽  
Instructional Design ◽  
Learning Environment ◽  
Learning Environments ◽  
Learning Models ◽  
Online Learning Environments ◽  
Design Models ◽  
The Past ◽  
Environment Assessment ◽  
Five Elements

Based on constructivist principles, this chapter provides a new instructional design map for online learning environments. This instructional design map includes considerations of five elements, namely, learner, knowledge, learning environment, assessment, and technology. Considerations of these elements are based on analyses of the past and existing instructional design models, online learning models, and constructive principles. Applications of the instructional design map are also discussed in the chapter.

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