A non-linear dimensionality-reduction technique for fast similarity search in large databases

2006 ◽  
Author(s):  
Khanh Vu ◽  
Kien A. Hua ◽  
Hao Cheng ◽  
Sheau-Dong Lang
Keyword(s):  
Dimensionality Reduction ◽  
Similarity Search ◽  
Reduction Technique ◽  
Large Databases ◽  
Dimensionality Reduction Technique ◽  
Non Linear ◽  
Linear Dimensionality Reduction

scholarly journals Semi-Supervised Non-Linear Dimensionality Reduction Technique for Sentiment Analysis Classification

2019 ◽  
Vol 8 (9) ◽  
pp. 1721-1725
Keyword(s):  
Dimensionality Reduction ◽  
Sentiment Analysis ◽  
Reduction Technique ◽  
Sentiment Classification ◽  
Random Subspace ◽  
Wide Scope ◽  
Dimensionality Reduction Technique ◽  
Non Linear ◽  
Linear Dimensionality Reduction ◽  
The Web

With the quick development in data advances, client created substance, for example, reviews, ratings, recommendations can be advantageously posted on the web, which have powered enthusiasm for sentiment classification. The quantity of records accessible on both online and offline is expanding drastically. Sentiment Classification has a wide scope of utilizations in review related sites. In this paper, we present our investigations about some exploration paper in this field and exhibited our plan to distinguish the sentiment extremity of a given content as positive or negative by lessening the documents dimension, through utilizing semi-supervised non-linear dimensionality decrease technique. For Sentiment Classification, Random Subspace strategy is utilized. For exploratory assessment, openly accessible sentiment datasets can be utilized to check the adequacy of the proposed technique.

scholarly journals Evaluation of UMAP as an alternative to t-SNE for single-cell data

2018 ◽  
Author(s):  
Etienne Becht ◽  
Charles-Antoine Dutertre ◽  
Immanuel W. H. Kwok ◽  
Lai Guan Ng ◽  
Florent Ginhoux ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Rna Sequencing ◽  
Reduction Technique ◽  
High Dimensional ◽  
The Past ◽  
Dimensionality Reduction Technique ◽  
Single Cell Rna Sequencing ◽  
Linear Dimensionality Reduction ◽  
Cell Data

AbstractUniform Manifold Approximation and Projection (UMAP) is a recently-published non-linear dimensionality reduction technique. Another such algorithm, t-SNE, has been the default method for such task in the past years. Herein we comment on the usefulness of UMAP high-dimensional cytometry and single-cell RNA sequencing, notably highlighting faster runtime and consistency, meaningful organization of cell clusters and preservation of continuums in UMAP compared to t-SNE.

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