Learning Optimal Kernel from Distance Metric in Twin Kernel Embedding for Dimensionality Reduction and Visualization of Fingerprints

2007 ◽  
pp. 227-238
Author(s):  
Yi Guo ◽  
Paul W. Kwan ◽  
Junbin Gao
Keyword(s):  
Dimensionality Reduction ◽  
Distance Metric ◽  
Optimal Kernel

Robust dimensionality reduction via feature space to feature space distance metric learning

2019 ◽  
Vol 112 ◽  
pp. 1-14 ◽  
Author(s):  
Bo Li ◽  
Zhang-Tao Fan ◽  
Xiao-Long Zhang ◽  
De-Shuang Huang
Keyword(s):  
Dimensionality Reduction ◽  
Metric Learning ◽  
Feature Space ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Space Distance

scholarly journals NEW SUPERVISED LOCALLY LINEAR EMBEDDING FOR DIMENSIONALITY REDUCTION USING DISTANCE METRIC LEARNING

2016 ◽  
Vol 26 (5) ◽  
pp. 449-459
Author(s):  
Bo Yang ◽  
Ming Xiang ◽  
Yupei Zhan ◽  
Shanmin Pang ◽  
Xin Li
Keyword(s):  
Dimensionality Reduction ◽  
Metric Learning ◽  
Locally Linear Embedding ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Linear Embedding ◽  
Locally Linear

Mixture Factor Analysis with Distance Metric Constraint for Dimensionality Reduction

2021 ◽  
pp. 108156
Author(s):  
Jian Chen ◽  
Leiyao Liao ◽  
Wei Zhang ◽  
Lan Du
Keyword(s):  
Factor Analysis ◽  
Dimensionality Reduction ◽  
Distance Metric

scholarly journals A visual analytic approach for the identification of ICU patient subpopulations using ICD diagnostic codes

2021 ◽  
Vol 7 ◽  
pp. e430
Author(s):  
Daniel Alcaide ◽  
Jan Aerts
Keyword(s):  
Dimensionality Reduction ◽  
Visual Analytics ◽  
International Classification Of Diseases ◽  
Reduction Technique ◽  
Distance Metric ◽  
Patient Profile ◽  
Clinical Procedures ◽  
Diagnostic Codes ◽  
Dimensionality Reduction Technique ◽  
Classification Of Diseases

A large number of clinical concepts are categorized under standardized formats that ease the manipulation, understanding, analysis, and exchange of information. One of the most extended codifications is the International Classification of Diseases (ICD) used for characterizing diagnoses and clinical procedures. With formatted ICD concepts, a patient profile can be described through a set of standardized and sorted attributes according to the relevance or chronology of events. This structured data is fundamental to quantify the similarity between patients and detect relevant clinical characteristics. Data visualization tools allow the representation and comprehension of data patterns, usually of a high dimensional nature, where only a partial picture can be projected. In this paper, we provide a visual analytics approach for the identification of homogeneous patient cohorts by combining custom distance metrics with a flexible dimensionality reduction technique. First we define a new metric to measure the similarity between diagnosis profiles through the concordance and relevance of events. Second we describe a variation of the Simplified Topological Abstraction of Data (STAD) dimensionality reduction technique to enhance the projection of signals preserving the global structure of data. The MIMIC-III clinical database is used for implementing the analysis into an interactive dashboard, providing a highly expressive environment for the exploration and comparison of patients groups with at least one identical diagnostic ICD code. The combination of the distance metric and STAD not only allows the identification of patterns but also provides a new layer of information to establish additional relationships between patient cohorts. The method and tool presented here add a valuable new approach for exploring heterogeneous patient populations. In addition, the distance metric described can be applied in other domains that employ ordered lists of categorical data.

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

Nonlinear Dimensionality Reduction for Data on Manifold with Rings

2009 ◽  
Vol 19 (11) ◽  
pp. 2908-2920
Author(s):  
De-Yu MENG ◽  
Nan-Nan GU ◽  
Zong-Ben XU ◽  
Yee LEUNG
Keyword(s):  
Dimensionality Reduction ◽  
Nonlinear Dimensionality Reduction
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