Intelligent Database Systems Lab Presenter: YU-TING LU Authors: Junping Zhang, Hua Huang and Jue Wang IEEE INTELLIGENT SYSTEMS Manifold Learning for Visualizing and Analyzing High-Dimensional Data

Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Applications Conclusions Comments

Intelligent Database Systems Lab Motivation Handling large-scale, high-dimensional data is a challenging task in modern statistical data analysis. Conventional approaches do not scale well to more than a few dozen dimensions. Linear projection is still inadequate in capturing interesting structures in data if the data do not live in a linear subspace.

Intelligent Database Systems Lab Objectives Describes a few representative manifold-learning algorithms. Demonstrates their utility in visualizing and analyzing high-dimensional data. Discuss their strengths and weaknesses, along with strategies to avoid pitfalls.

Intelligent Database Systems Lab Methodology

Intelligent Database Systems Lab Methodology

Intelligent Database Systems Lab Applications

Intelligent Database Systems Lab Conclusions Manifold-learning algorithms are problematic if data properties are not properly considered. There must be more quantitative methods available to evaluate manifold-learning algorithms and better ways to automatically select intrinsic dimensions.

Intelligent Database Systems Lab Comments Advantages - Faithfully visualizing non-metric similarity data Applications - Data visualization.