Effect of domain selection for compact representation of spatial variation of Head-Related Transfer Function in all directions based on Spatial Principal Components Analysis

Principal Components ◽

Compact Representation ◽

Head Related Transfer Function ◽

Components Analysis ◽

Domain Selection ◽

Comparison of Head-related Transfer Function Models Based on Principal Components Analysis

Transactions of the Korean Society for Noise and Vibration Engineering ◽

10.5050/ksnvn.2008.18.6.642 ◽

2008 ◽

Vol 18 (6) ◽

pp. 642-653 ◽

Cited By ~ 2

Keyword(s):

Transfer Function ◽

Principal Components ◽

Head Related Transfer Function ◽

Components Analysis ◽

Function Models ◽

Transfer Function Models

Principal Components Analysis of Temporal and Spatial Variation in Trichopteran Faunas of Northen Portugal

Proceedings of the Fifth International Symposium on Trichoptera ◽

10.1007/978-94-009-4043-7_56 ◽

1987 ◽

pp. 313-317

Author(s):

M. C. Molles ◽

L. S. W. Terra

Keyword(s):

Spatial Variation ◽

Principal Components ◽

Temporal And Spatial Variation ◽

Components Analysis ◽

Temporal And Spatial

Some investigations on properties of spatial principal components analysis of individual head-related transfer functions

The Journal of the Acoustical Society of America ◽

10.1121/1.4970284 ◽

2016 ◽

Vol 140 (4) ◽

pp. 3250-3250

Author(s):

Shouichi Takane

Keyword(s):

Principal Components ◽

Transfer Functions ◽

Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08 ◽

Unsupervised feature selection for principal components analysis

10.1145/1401890.1401903 ◽

2008 ◽

Cited By ~ 37

Author(s):

Christos Boutsidis ◽

Michael W. Mahoney ◽

Petros Drineas

Keyword(s):

Feature Selection ◽

Principal Components ◽

Unsupervised Feature Selection ◽

Components Analysis ◽

Knowledge, Information, and Creativity Support Systems - Lecture Notes in Computer Science ◽

Band Selection for Hyperspectral Image Using Principal Components Analysis and Maxima-Minima Functional

10.1007/978-3-642-24788-0_10 ◽

2011 ◽

pp. 103-112 ◽

Cited By ~ 4

Author(s):

Kitti Koonsanit ◽

Chuleerat Jaruskulchai

Keyword(s):

Principal Components ◽

Hyperspectral Image ◽

Band Selection ◽

Components Analysis ◽

Measurement processes and spatial principal components analysis

Brain Topography ◽

10.1007/bf01135564 ◽

1992 ◽

Vol 4 (4) ◽

pp. 267-276 ◽

Cited By ~ 26

Author(s):

Richard B. Silberstein ◽

Peter J. Cadusch

Keyword(s):

Principal Components ◽

Assessing the indicators of economic growth in Indonesia using spatial principal components analysis

Journal of Physics Conference Series ◽

10.1088/1742-6596/1446/1/012051 ◽

2020 ◽

Vol 1446 ◽

pp. 012051

Author(s):

A Karim ◽

T W Utami ◽

A Fathurohman ◽

C I Setyowati ◽

Suhartono ◽

...

Keyword(s):

Economic Growth ◽

Principal Components ◽

Band Selection for Dimension Reduction in Hyper Spectral Image Using Integrated InformationGain and Principal Components Analysis Technique

International Journal of Machine Learning and Computing ◽

10.7763/ijmlc.2012.v2.124 ◽

2012 ◽

pp. 248-251 ◽

Cited By ~ 36

Author(s):

Kitti Koonsanit ◽

Chuleerat Jaruskulchai ◽

Apisit Eiumnoh

Keyword(s):

Dimension Reduction ◽

Principal Components ◽

Band Selection ◽

Spectral Image ◽

Hyper Spectral ◽

Analysis Technique ◽

Components Analysis ◽

Spatial principal components analysis on head-related transfer functions and individualized customization from fewer measurements.

The Journal of the Acoustical Society of America ◽

10.1121/1.3249214 ◽

2009 ◽

Vol 126 (4) ◽

pp. 2242

Author(s):

Bo-sun Xie

Keyword(s):

Principal Components ◽

Transfer Functions ◽

Parental Line Selection for Cucumber Hybrid Seed Production by Principal Components Analysis

International Journal of Vegetable Science ◽

10.1080/19315260.2010.482578 ◽

2010 ◽

Vol 16 (4) ◽

pp. 316-325

Author(s):

J. A. Olfati ◽

H. Samizadeh ◽

Gh. Peyvast ◽

B. Rabiei ◽

S. A. Khodaparast

Keyword(s):

Seed Production ◽

Principal Components ◽

Hybrid Seed ◽

Parental Line ◽

Hybrid Seed Production ◽

Line Selection ◽

Components Analysis ◽