Matrix Rank Reduction for Data Analysis and Feature Extraction

PurposeThis study intends to examine consumers' fashion customization experiences through a web content mining (WCM) approach. By applying the theory of customer value, this study explores the benefits and costs of two levels of mass customization (MC) to identify the values derived from style (i.e. shoe customization) and fit customization experiences (i.e. apparel customization) and further to compare the dominating dimensions of value derived across style and fit customization.Design/methodology/approachA WCM approach was applied. Also, two case studies were conducted with one focusing on style customization and the other focusing on fit customization. The brand Vans was selected to examine style customization in study 1. The brand Sumissura was selected to examine fit customization in study 2. Consumers' comments on customization experiences from these two brands were collected through social networks, respectively. After data cleaning, 394 reviews for Vans and 510 reviews for Sumissura were included in the final data analysis. Co-occurrence plots, feature extraction and grouping were used for the data analysis.FindingsThe emotional value was found to be the major benefit for style customization, while the functional value was indicated as the major benefit for fit customization, followed by ease of use and emotional value. In addition, three major themes of costs, including unsatisfied service, disappointing product performance and financial risk, were revealed by excavating and evaluating consumers' feedback of their actual clothing customization experiences with Sumissura.Originality/valueThis study initiates the effort to use web mining, specifically, the WCM approach to thoroughly investigate the benefits and costs of MC through real consumers' feedback of two different types of fashion products. The analysis of this study also reflects the levels of customization: style and fit. It provides an in-depth text analysis of online MC consumers' feedback through the use of feature extraction analysis and word co-occurrence networks.

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Convolutional Autoencoder Based Feature Extraction in Radar Data Analysis

2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS) ◽

10.1109/scis-isis.2018.00023 ◽

2018 ◽

Author(s):

Hansoo Lee ◽

Jonggeun Kim ◽

Baekcheon Kim ◽

Sungshin Kim

Keyword(s):

Feature Extraction ◽

Data Analysis ◽

Radar Data ◽

Convolutional Autoencoder

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Coherent noise estimation by adaptive Hankel matrix rank reduction

10.1190/segam2012-0063.1 ◽

2012 ◽

Cited By ~ 1

Author(s):

Nirupama (Pam) Nagarajappa

Keyword(s):

Hankel Matrix ◽

Noise Estimation ◽

Rank Reduction ◽

Coherent Noise ◽

Matrix Rank

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Optimization Audicor for Normal and Abnormal Heart Sounds Characteristic

International Journal on Data Science ◽

10.18517/ijods.1.2.99-106.2020 ◽

2020 ◽

Vol 1 (2) ◽

pp. 99-106

Author(s):

Dedi Kurniadi ◽

Surfa Yondri ◽

Albar ◽

Roza Susanti ◽

David Eka Putra ◽

...

Keyword(s):

Signal Processing ◽

Feature Extraction ◽

Data Analysis ◽

Ventricular Septal Defect ◽

Human Body ◽

Septal Defect ◽

Heart Sound ◽

Heart Sounds ◽

Normal Case ◽

Recording Process

Heart Sounds are important things in the human body that can deliver information related to the heart condition. However, a recorded signal such as PCG and ECG that getting through Audicor still contain unexpected components or noise while the recording process happens it makes the result data from Audicor cannot directly use to recognize the condition of the heart. This research presents signal processing and data analysis to suppress the noise of the heart sounds that getting while the process of recording data happens. The cleaned heart sound will be processed in feature extraction by using FFT and PCA that capable to produce the feature both of the normal and abnormal heart sounds. For the normal case, we get the data from some healthy volunteers recorded by using Audicor. While the abnormal heart sound we focus to observe the data that contain Ventricular Septal Defect (VSD) that getting from a partner hospital. As a result, feature both normal and abnormal heart sounds can be separated.

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Functional and QoS Semantics-Driven SOA-Based Biomedical Multimedia Processing

Non-Functional Properties in Service Oriented Architecture ◽

10.4018/978-1-60566-794-2.ch015 ◽

2011 ◽

pp. 335-359

Author(s):

Shih-Hsi Liu ◽

Yu Cao ◽

Ming Li ◽

Thell Smith ◽

John Harris ◽

...

Keyword(s):

Machine Learning ◽

Medical Education ◽

Feature Extraction ◽

Data Analysis ◽

Requirements Elicitation ◽

Multimedia Data ◽

Multimedia Processing ◽

Wide Range ◽

Processing Component

Although there have existed a wide range of techniques of biomedical multimedia processing, none of them could be generally satisfied by various domains. The main reason for such deficiency is due to the correlative nature between biomedical multimedia data and the techniques applied to them. This book chapter introduces an SOA-based biomedical multimedia infrastructure with a pre-processing component. Such an infrastructure adapts the concepts of requirements elicitation of Software Engineering as well as a training set of Machine Learning to analyze functional and QoS properties of biomedical multimedia data in advance. Such properties will be constructed as ontology and used for selecting the most appropriate services to perform data analysis, transmission, or retrieval. Two medical education projects are introduced as case studies to illustrate the usage of functional and QoS semantics extracted from a feature extraction service to improve the performance of subsequent classification service and searching service, respectively.

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