Guided Projections for Analyzing the Structure of High-Dimensional Data

Ortner, Thomas; Filzmoser, Peter; Rohm, Maia; Breiteneder, Christian; Brodinova, Sarka

doi:10.1080/10618600.2018.1459304

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…In recent years, there has been an exponential increase in the amount of digital information being generated across various fields, which has led to a significant surge in the size, complexity, diversity, and dimensions of data [1], which has given rise to a new type of data known as high dimensional data (HDD) [2], [3] HDD has been widely utilized across various industries, including healthcare, the Internet, education, commerce, and social networking [4], to name a few. The ever-increasing availability of new high-dimensional data can take on various formats, such as text [5], digital images [6], speech signals [7], and videos [8], among others.…”

Section: Introductionmentioning

confidence: 99%

A hybrid model for data visualization using linear algebra methods and machine learning algorithm

Ali,

Choudhary,

Kasbe

2024

IJEECS

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The t-distributed stochastic neighbor embedding (t-SNE) is a powerful technique for visualizing high-dimensional datasets. By reducing the dimensionality of the data, t-SNE transforms it into a format that can be more easily understood and analyzed. The existing approach is to visualize high-dimensional data but not deeply visualize. This paper proposes a model that enhances visualization and improves the accuracy. The proposed model combines the non-linear embedding technique t-SNE, the linear dimensionality reduction method principal component analysis (PCA), and the QR decomposition algorithm for discovering eigenvalues and eigenvectors. In Addition, we quantitatively compare the proposed model QRPCA-t-SNE with PCA-t-SNE using the following criteria: data visualization with different perplexity and different principal components, confusion matrix, model score, mean square error (MSE), training, testing accuracy, receiver operating characteristic curve (ROC) score, and AUC score.

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Section: Introductionmentioning

confidence: 99%

A hybrid model for data visualization using linear algebra methods and machine learning algorithm

Ali,

Choudhary,

Kasbe

2024

IJEECS

View full text Add to dashboard Cite

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“…The proposed approach aims at combining these two aspects by defining projections based on the local neighborhood of an observation where no reliable assumption about the data structure can be made and by considering the concept of the orthogonal complement similar to ROBPCA. The approach of local projections is an extension of Guided projections for analyzing the structure of high-dimensional data (Ortner et al [19]). We identify a subset of observations locally, describing the structure of a dataset in order to evaluate the outlyingness of other nearby observations.…”

Section: Introductionmentioning

confidence: 99%

Local projections for high-dimensional outlier detection

Ortner¹,

Filzmoser²,

Rohm

et al. 2020

METRON

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A novel approach for outlier detection is proposed, called local projections, which is based on concepts of the Local Outlier Factor (LOF) (Breunig et al. in Lof: identifying densitybased local outliers. In: ACM sigmod record, ACM, volume 29, pp. 93-104, 2000) and ROBPCA (Hubert et al. in Technometrics 47(1):64-79, 2005). By using aspects of both methods, this algorithm is robust towards noise variables and is capable of performing outlier detection in multi-group situations. The idea is to focus on local descriptions of the observations and their neighbors using linear projections. The outlyingness of an observation is determined by a weighted distance of the observation to all identified projection spaces, with weights depending on the appropriateness of the local description. Experiments with simulated and real data demonstrate the usefulness of this method when compared to existing outlier detection algorithms.

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Guided Projections for Analyzing the Structure of High-Dimensional Data

Abstract: A powerful data transformation method named guided projections is proposed creating new possibilities to reveal the group structure of high-dimensional data in the presence of noise variables. Utilising projections onto a space spanned by a selection of a small number *

Cited by 2 publications

References 33 publications

A hybrid model for data visualization using linear algebra methods and machine learning algorithm

A hybrid model for data visualization using linear algebra methods and machine learning algorithm

Local projections for high-dimensional outlier detection

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