Dimension Reduction for Visual Data Mining

Immersive Interactive Information Mining with Application to Earth Observation Data Retrieval

The exponentially increasing amount of Earth Observation (EO) data requires novel approaches for data mining and exploration. Visual analytic systems have made valuable contribution in understanding the structure of data by providing humans with visual perception of data. However, these systems have limitations in dealing with large-scale high-dimensional data. For instance, the limitation in d...

Graph Embedding and Dimensionality Reduction - A Survey

Dimension reduction is defined as the process of mapping high-dimensional data to a lowerdimensional vector space. Most machine learning and data mining techniques may not be effective for high-dimensional data. In order to handle this data adequately, its dimensionality needs to be reduced. Dimensionality reduction is also needed for visualization, graph embedding, image retrieval and a variet...

Dimension Reduction Algorithms for Data Mining and Visualization

Study on Dimensionality Reduction Techniques and Applications

Data is not collected only for data mining. Data accumulates in an unprecedented speed. Data preprocessing is an important part for effective machine learning and data mining. Data mining is discovering interesting knowledge from large amounts of data, which is the integral part of the KDD (Knowledge Discovery in Databases), which is the overall process of converting raw data into useful inform...

Multilevel Linear Dimensionality Reduction for Data Analysis using Nearest-Neighbor Graphs

Dimension reduction techniques can be time-consuming when the data set is large. This paper presents a multilevel framework to reduce the size of the data set, prior to performing dimension reduction. The algorithm exploits nearestneighbor graphs. It recursively coarsens the data by finding a maximal matching level by level. Once the coarse graph is available, the coarsened data is projected at...