scholarly journals Dimensionality reduction and similarity computation by inner-product approximations

2004 ◽  
Vol 16 (6) ◽  
pp. 714-726 ◽  
Author(s):  
O. Egecioglu ◽  
H. Ferhatosmanoglu ◽  
U. Ogras
Keyword(s):  
Dimensionality Reduction ◽  
Inner Product ◽  
Similarity Computation

scholarly journals Learning-Based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching

2020 ◽  
Vol 34 (04) ◽  
pp. 3219-3226 ◽  
Author(s):  
Yunsheng Bai ◽  
Hao Ding ◽  
Ken Gu ◽  
Yizhou Sun ◽  
Wei Wang
Keyword(s):  
Inner Product ◽  
Approximate Methods ◽  
Graph Similarity ◽  
Fine Grained ◽  
Multi Scale ◽  
As Graph ◽  
Similarity Computation ◽  
Search Graph ◽  
Model Graph ◽  
Node Embeddings

Graph similarity computation is one of the core operations in many graph-based applications, such as graph similarity search, graph database analysis, graph clustering, etc. Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a trade-off between accuracy and speed. Recently, several data-driven approaches based on neural networks have been proposed, most of which model the graph-graph similarity as the inner product of their graph-level representations, with different techniques proposed for generating one embedding per graph. However, using one fixed-dimensional embedding per graph may fail to fully capture graphs in varying sizes and link structures—a limitation that is especially problematic for the task of graph similarity computation, where the goal is to find the fine-grained difference between two graphs. In this paper, we address the problem of graph similarity computation from another perspective, by directly matching two sets of node embeddings without the need to use fixed-dimensional vectors to represent whole graphs for their similarity computation. The model, Graph-Sim, achieves the state-of-the-art performance on four real-world graph datasets under six out of eight settings (here we count a specific dataset and metric combination as one setting), compared to existing popular methods for approximate Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) computation.

scholarly journals An Efficient Kriging Modeling Method Based on Multidimensional Scaling for High-Dimensional Problems

Algorithms ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 3
Author(s):  
Yu Ge ◽  
Junjun Shi ◽  
Yaohui Li ◽  
Jingfang Shen
Keyword(s):  
Multidimensional Scaling ◽  
Dimensionality Reduction ◽  
Flight Control ◽  
Distance Matrix ◽  
Modeling Method ◽  
Inner Product ◽  
High Dimensional ◽  
Design Data ◽  
Field Problem ◽  
Product Matrix

Kriging-based modeling has been widely used in computationally intensive simulations. However, the Kriging modeling of high-dimensional problems not only takes more time, but also leads to the failure of model construction. To this end, a Kriging modeling method based on multidimensional scaling (KMDS) is presented to avoid the “dimensional disaster”. Under the condition of keeping the distance between the sample points before and after the dimensionality reduction unchanged, the KMDS method, which mainly calculates each element in the inner product matrix due to the mapping relationship between the distance matrix and the inner product matrix, completes the conversion of design data from high dimensional to low dimensional. For three benchmark functions with different dimensions and the aviation field problem of aircraft longitudinal flight control, the proposed method is compared with other dimensionality reduction methods. The KMDS method has better modeling efficiency while meeting certain accuracy requirements.

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

СONTRIBUTION OF POLYMORPHIC VARIATION OF ТP53 AND XRCC1 GENES TO THE SUSCEPTIBILITY TO BREAST CANCER FOR WOMEN OF KYRGYZ AND BELARUSIAN NATIONALITY - A COMPARATIVE STUDY ON MULTIFACTOR DIMENSIONALITY REDUCTION METHODS

2018 ◽  
Vol 64 (1) ◽  
pp. 95-101
Author(s):  
Nazira Aldasheva ◽  
Vyacheslav Kipen ◽  
Zhaynagul Isakova ◽  
Sergey Melnov ◽  
Raisa Smolyakova ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Dimensionality Reduction ◽  
Multifactor Dimensionality Reduction ◽  
Rflp Analysis ◽  
Kyrgyz Republic ◽  
Increased Risk ◽  
Polymorphic Variants ◽  
The Republic

Basing on Multifactor Dimensionality Reduction method we showed that polymorphic variants p.Q399R (rs25487, XRCC1) and p.P72R (rs1042522, TP53) correlated with increased risk of breast cancer for women from the Kyrgyz Republic and the Republic of Belarus. Cohort for investigation included patients with clinically verified breast cancer: 117 women from the Kyrgyz Republic (nationality - Kyrgyz) and 169 - of the Republic of Belarus (nationality - Belarusians). Group for comparison included (healthy patients without history of cancer pathology at the time of blood sampling) 102 patients from the Kyrgyz Republic, 185 - from the Republic of Belarus. Respectively genotyping of polymorphic variants p.Q399R (rs25487, XRCC1) and p.P72R (rs1042522, TP53) was done by PCR-RFLP. Analysis of the intergenic interactions conducted with MDR 3.0.2 software. Both ethnic groups showed an increase of breast cancer risk in the presence of alleles for SNPs Gln p.Q399R (XRCC1) in the heterozygous state: for the group “Kyrgyz” - OR=2,78 (95% CI=[1,60-4,82]), p=0,001; for the group “Belarusians” - OR=1,85 (95% СІ=[1Д1-2,82], p=0,004. Carriers with combination of alleles Gln (p.Q399R, XRCC1) and Pro (p.P72R, TP53) showed statistically significance increases of breast cancer risk as for patients from the Kyrgyz Republic (OR=2,89, 95% CI=[1,33-6,31]), so as for patients from the Republic of Belarus (OR=3,01, 95% CI=[0,79-11,56]).

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