Riemannian Manifold Clustering and Dimensionality Reduction for Vision-Based Analysis

2011 ◽  
pp. 27-53 ◽  
Author(s):  
Alvina Goh
Keyword(s):  
Riemannian Manifold ◽  
Dimensionality Reduction ◽  
Manifold Clustering

Unsupervised speaker diarization using riemannian manifold clustering

2014 ◽  
Author(s):  
Che-Wei Huang ◽  
Bo Xiao ◽  
Panayiotis G. Georgiou ◽  
Shrikanth S. Narayanan
Keyword(s):  
Riemannian Manifold ◽  
Speaker Diarization ◽  
Manifold Clustering

Enhanced Matrix CFAR Detection With Dimensionality Reduction of Riemannian Manifold

2020 ◽  
Vol 27 ◽  
pp. 2084-2088
Author(s):  
Zheng Yang ◽  
Yongqiang Cheng ◽  
Hao Wu ◽  
Hongqiang Wang
Keyword(s):  
Riemannian Manifold ◽  
Dimensionality Reduction ◽  
Cfar Detection

scholarly journals Segmentation of High Angular Resolution Diffusion MRI Using Sparse Riemannian Manifold Clustering

2014 ◽  
Vol 33 (2) ◽  
pp. 301-317 ◽  
Author(s):  
H. Ertan Cetingul ◽  
Margaret J. Wright ◽  
Paul M. Thompson ◽  
Rene Vidal
Keyword(s):  
Riemannian Manifold ◽  
Diffusion Mri ◽  
Angular Resolution ◽  
High Angular Resolution ◽  
Manifold Clustering

scholarly journals Dimensionality Reduction of SPD Data Based on Riemannian Manifold Tangent Spaces and Isometry

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1117
Author(s):  
Wenxu Gao ◽  
Zhengming Ma ◽  
Weichao Gan ◽  
Shuyu Liu
Keyword(s):  
Riemannian Manifold ◽  
Euclidean Space ◽  
Dimensionality Reduction ◽  
Tangent Space ◽  
Euclidean Distance ◽  
Geodesic Distance ◽  
Tangent Vector ◽  
Identity Matrix ◽  
Affine Invariant ◽  
Bilinear Transformation

Symmetric positive definite (SPD) data have become a hot topic in machine learning. Instead of a linear Euclidean space, SPD data generally lie on a nonlinear Riemannian manifold. To get over the problems caused by the high data dimensionality, dimensionality reduction (DR) is a key subject for SPD data, where bilinear transformation plays a vital role. Because linear operations are not supported in nonlinear spaces such as Riemannian manifolds, directly performing Euclidean DR methods on SPD matrices is inadequate and difficult in complex models and optimization. An SPD data DR method based on Riemannian manifold tangent spaces and global isometry (RMTSISOM-SPDDR) is proposed in this research. The main contributions are listed: (1) Any Riemannian manifold tangent space is a Hilbert space isomorphic to a Euclidean space. Particularly for SPD manifolds, tangent spaces consist of symmetric matrices, which can greatly preserve the form and attributes of original SPD data. For this reason, RMTSISOM-SPDDR transfers the bilinear transformation from manifolds to tangent spaces. (2) By log transformation, original SPD data are mapped to the tangent space at the identity matrix under the affine invariant Riemannian metric (AIRM). In this way, the geodesic distance between original data and the identity matrix is equal to the Euclidean distance between corresponding tangent vector and the origin. (3) The bilinear transformation is further determined by the isometric criterion guaranteeing the geodesic distance on high-dimensional SPD manifold as close as possible to the Euclidean distance in the tangent space of low-dimensional SPD manifold. Then, we use it for the DR of original SPD data. Experiments on five commonly used datasets show that RMTSISOM-SPDDR is superior to five advanced SPD data DR algorithms.

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.

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