An empirical analysis of graph‐based linear dimensionality reduction techniques

Abstract

Summary Many emerging applications such as social networks have prompted remarkable attention in graph data analysis. Graph data is typically high‐dimensional in nature, and dimensionality reduction is critical regarding storage, analysis, and querying of such data efficiently. Although there are many dimensionality reduction methods, it is not clear to what extent the performances of the various dimensionality reduction techniques differ. In this article, we review some of the well‐known linear dimensionality reduction methods and perform an empirical analysis of these approaches using large multidimensional graph datasets. Our results show that in linear unsupervised learning methods, the principal component analysis, singular value decomposition, and neighborhood preserving embedding methods achieve better retrieval data performance than other methods of the statistical information category, dictionary methods, and embedding methods, respectively. Regarding supervised learning methods, the experimental results demonstrate that linear discriminant analysis and partial least squares presented almost similar results.

A Survey on Dimensionality Reduction Techniques for Time-Series Data

“…Principal Component Analysis (PCA) is an unsupervised technique that is one of the widely used dimensionality reduction techniques for dealing with the high dimensionality for time-series data [41]. It is a linear transformation method and computationally less expensive [42]. As a result, applying PCA helps to run the MLAs faster while the structure of the original dataset is sustained [43].…”