scholarly journals A 2D Expansion Method Using Fractal Shapes for High Dimensional Data

2009 ◽  
Vol 29-1 (2) ◽  
pp. 1319-1319
Author(s):  
Takanori Fujiwara ◽  
Manabu Tange ◽  
Satoshi Someya ◽  
Koji Okamoto
Keyword(s):  
High Dimensional Data ◽  
Expansion Method ◽  
High Dimensional

Fractal Map: Fractal-Based 2D Expansion Method for Multi-scale High-Dimensional Data Visualization

2010 ◽  
pp. 306-315 ◽  
Author(s):  
Takanori Fujiwara ◽  
Ryo Matsushita ◽  
Masaki Iwamaru ◽  
Manabu Tange ◽  
Satoshi Someya ◽  
...  
Keyword(s):  
Data Visualization ◽  
High Dimensional Data ◽  
Expansion Method ◽  
High Dimensional ◽  
Multi Scale

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional

Approximate Cluster Heat Maps of Large High-Dimensional Data

2018 ◽  
Author(s):  
Punit Rathore ◽  
James C. Bezdek ◽  
Dheeraj Kumar ◽  
Sutharshan Rajasegarar ◽  
Marimuthu Palaniswami
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Heat Maps

scholarly journals The Generalized Bayes Method for High-Dimensional Data Recognition with Applications to Audio Signal Recognition

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 19
Author(s):  
Hsiuying Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Conventional Method ◽  
High Dimensional Data ◽  
Audio Signal ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Signal Recognition ◽  
Bayes Method ◽  
Generalized Bayes

High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application.

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