Abstract To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders magnitude smaller than dimensionality these data, valid inferences require finding a low-dimensional representation p...

Plant has plenty use in foodstuff, medicine and industry, and is also vitally important for environmental protection. So, it is important and urgent to recognize and classify plant species. Plant classification based on leaf images is a basic research of botanical area and agricultural production. Due to the high nature complexity and high dimensionality of leaf image data, dimensional reductio...

Nonlinear dimensionality reduction (DR) techniques offer the possibility to visually inspect a high-dimensional data set in two dimensions, and such methods have recently been extended to also visualize class boundaries as induced by a trained classifier on the data. In this contribution, we investigate the effect of two different ways to shape the involved dimensionality reduction technique in...

Indexing issues that arise in the support of similarity searching are presented. This includes a discussion of the curse of dimensionality, as well as multidimensional indexing, distance-based indexing, dimension reduction, and embedding methods.

Our objective is to learn representations for the shape and the appearance of moving (dynamic) objects that support tasks such as synthesis, pose recovery, reconstruction, and tracking. In this paper, we introduce a framework that aim to learn a landmark-free correspondence-free global representations of dynamic appearance manifolds. We use nonlinear dimensionality reduction to achieve an embed...