Recent Dimensionality Reduction Techniques for Visualizing High-Dimensional Parkinson’s Disease Omics Data

AbstractEmerging single-cell technologies profile multiple types of molecules within individual cells. A fundamental step in the analysis of the produced high-dimensional data is their visualization using dimensionality reduction techniques such as t-SNE and UMAP. We introduce j-SNE and j-UMAP as their natural generalizations to the joint visualization of multimodal omics data. Our approach automatically learns the relative contribution of each modality to a concise representation of cellular identity that promotes discriminative features but suppresses noise. On eight datasets, j-SNE and j-UMAP produce unified embeddings that better agree with known cell types and that harmonize RNA and protein velocity landscapes.

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A SURVEY ON THE CURES FOR THE CURSE OF DIMENSIONALITY IN BIG DATA

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2017.v10s1.19755 ◽

2017 ◽

Vol 10 (13) ◽

pp. 355 ◽

Cited By ~ 1

Author(s):

Reshma Remesh ◽

Pattabiraman. V

Keyword(s):

Dimensionality Reduction ◽

Input Data ◽

Principal Component ◽

Kernel Principal Component Analysis ◽

High Dimensional ◽

Data Sets ◽

Learning Approaches ◽

Data Set ◽

Reduction Techniques ◽

Dimensionality Reduction Techniques

Dimensionality reduction techniques are used to reduce the complexity for analysis of high dimensional data sets. The raw input data set may have large dimensions and it might consume time and lead to wrong predictions if unnecessary data attributes are been considered for analysis. So using dimensionality reduction techniques one can reduce the dimensions of input data towards accurate prediction with less cost. In this paper the different machine learning approaches used for dimensionality reductions such as PCA, SVD, LDA, Kernel Principal Component Analysis and Artificial Neural Network have been studied.

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Performance Analysis of Dimensionality Reduction Techniques in the Context of Clustering

Asian Journal of Computer Science and Technology ◽

10.51983/ajcst-2019.8.s3.2084 ◽

2019 ◽

Vol 8 (S3) ◽

pp. 66-71

Author(s):

T. Sudha ◽

P. Nagendra Kumar

Keyword(s):

Principal Component Analysis ◽

Dimensionality Reduction ◽

High Dimensional Data ◽

Principal Component ◽

Component Analysis ◽

High Dimensional ◽

Reduction Techniques ◽

Dimensionality Reduction Techniques ◽

Low Dimensional ◽

Probabilistic Principal Component Analysis

Data mining is one of the major areas of research. Clustering is one of the main functionalities of datamining. High dimensionality is one of the main issues of clustering and Dimensionality reduction can be used as a solution to this problem. The present work makes a comparative study of dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis in the context of clustering. High dimensional data have been reduced to low dimensional data using dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis. Cluster analysis has been performed on the high dimensional data as well as the low dimensional data sets obtained through t-distributed stochastic neighbour embedding and Probabilistic principal component analysis with varying number of clusters. Mean squared error; time and space have been considered as parameters for comparison. The results obtained show that time taken to convert the high dimensional data into low dimensional data using probabilistic principal component analysis is higher than the time taken to convert the high dimensional data into low dimensional data using t-distributed stochastic neighbour embedding.The space required by the data set reduced through Probabilistic principal component analysis is less than the storage space required by the data set reduced through t-distributed stochastic neighbour embedding.

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Analysis of unsupervised dimensionality reduction techniques

Computer Science and Information Systems ◽

10.2298/csis0902217k ◽

2009 ◽

Vol 6 (2) ◽

pp. 217-227 ◽

Cited By ~ 29

Author(s):

Aswani Kumar

Keyword(s):

Dimensionality Reduction ◽

Approximation Error ◽

High Dimensional ◽

Retrieval Task ◽

Document Collections ◽

Noise Effects ◽

Reduction Techniques ◽

Dimensionality Reduction Techniques ◽

Text Images ◽

High Dimensional Datasets

Domains such as text, images etc contain large amounts of redundancies and ambiguities among the attributes which result in considerable noise effects (i.e. the data is high dimension). Retrieving the data from high dimensional datasets is a big challenge. Dimensionality reduction techniques have been a successful avenue for automatically extracting the latent concepts by removing the noise and reducing the complexity in processing the high dimensional data. In this paper we conduct a systematic study on comparing the unsupervised dimensionality reduction techniques for text retrieval task. We analyze these techniques from the view of complexity, approximation error and retrieval quality with experiments on four testing document collections.

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Performance Evaluation of Dimensionality Reduction Techniques on High Dimensional Data

2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) ◽

10.1109/icoei.2019.8862526 ◽

2019 ◽

Author(s):

Mandikal Vikram ◽

Rakesh Pavan ◽

Navadiya Dhruvikkumar Dineshbhai ◽

Biju Mohan

Keyword(s):

Performance Evaluation ◽

Dimensionality Reduction ◽

High Dimensional Data ◽

High Dimensional ◽

Reduction Techniques ◽

Dimensionality Reduction Techniques

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A Review on Dimensionality Reduction Techniques

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001419500174 ◽

2019 ◽

Vol 33 (10) ◽

pp. 1950017 ◽

Cited By ~ 5

Author(s):

Xuan Huang ◽

Lei Wu ◽

Yinsong Ye

Keyword(s):

Pattern Recognition ◽

Dimensionality Reduction ◽

Implementation Process ◽

Recognition System ◽

Research Trend ◽

High Dimensional ◽

Open Problems ◽

Reduction Techniques ◽

Dimensionality Reduction Techniques ◽

Scope Of Application

High-dimensional data is ubiquitous in scientific research and industrial production fields. It brings a lot of information to people, at the same time, because of its sparse and redundancy, it also brings great challenges to data mining and pattern recognition. Dimensionality reduction can reduce redundancy and noise, reduce the complexity of learning algorithms, and improve the accuracy of classification, it is an important and key step in pattern recognition system. In this paper, we overview the classical techniques for dimensionality reduction and review their properties, and categorize these techniques according to their implementation process. We deduce each algorithm in detail and intuitively show their underlying mathematical principles. Thereby, the focus is to uncover the optimization process for each technique. We compare the characteristics and limitations of each technique and summarize the scope of application, discussing a number of open problems and a perspective of research trend in future.

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