scholarly journals Probabilistic inference of multi-Gaussian fields from indirect hydrological data using circulant embedding and dimensionality reduction

2015 ◽  
Vol 51 (6) ◽  
pp. 4224-4243 ◽  
Author(s):  
Eric Laloy ◽  
Niklas Linde ◽  
Diederik Jacques ◽  
Jasper A. Vrugt
Keyword(s):  
Dimensionality Reduction ◽  
Probabilistic Inference ◽  
Gaussian Fields ◽  
Hydrological Data ◽  
Circulant Embedding

Task information and performance in probabilistic inference tasks.

1976 ◽  
Author(s):  
Berndt Brehmer ◽  
Jan Kuylenstierna ◽  
Jan-Erik Liljergren
Keyword(s):  
Probabilistic Inference ◽  
And Performance

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.

Hydrological data and site feature information for a forest clearcutting and residual biomass removal study at the Marcell Experimental Forest

2020 ◽  
Author(s):  
Colin P. R. McCarter ◽  
Stephen D. Sebestyen ◽  
Susan L. Eggert ◽  
Kristine M. Haynes ◽  
Randall K. Kolka ◽  
...  
Keyword(s):  
Hydrological Data ◽  
Residual Biomass ◽  
Biomass Removal ◽  
Feature Information

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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