Cluster - preserving embedding of proteins by

Optimal Cluster Preserving Embedding of Non-Metri Proximity Data

Visualizing Graphs as Maps with Contiguous Regions

Relational datasets, which include clustering information, can be visualized with tools such as BubbleSets, LineSets, SOM, and GMap. The countries in SOM-based and GMap-based visualizations are fragmented, i.e., they are represented by several disconnected regions. While BubbleSets and LineSets have contiguous regions, these regions may overlap, even when the input clustering is non-overlapping...

Cluster - preserving embedding of proteins by Gabriela

Similarity searching in protein sequence databases is a standard technique for biologists dealing with a newly sequenced protein. Exhaustive search in such databases is prohibitive because of the large sizes of these database and because pairwise comparisons are slow. Heuristic techniques, such as FASTA and BLAST, are useful because they are fast and accurate, though it has been shown that exha...

Unsupervised Shape Clustering using Diffusion Maps

The quotient space of all smooth and connected curves represented by a fixed number of boundary points is a finite-dimensional Riemannian manifold, also known as a shape manifold. This makes the preservation of locality a critically important issue when reducing the dimensionality of shapes on the manifold. We present a completely unsupervised clustering algorithm employing diffusion maps for l...

Linear-Space Algorithms for Distance Preserving Embedding

The distance preserving graph embedding problem is to embed vertices of a given weighted graph into points in 2-dimensional Euclidean space so that for each edge the distance between their corresponding endpoints is as close to the weight of the edge as possible. If the given graph is complete, that is, if distance constraints are given as a full matrix, then principal coordinate analysis can s...