This paper describes an efficient technique for computing a hierarchical representation of the objects contained in a complex 3D scene. First, an adjacency graph keeping the costs of grouping the different pairs of objects in the scene is built. Then the minimum spanning tree (MST) of that graph is determined. A binary clustering tree (BCT) is obtained from the MST. Finally, a merging stage joi...

In hierarchical classification, classes are arranged in a hierarchy represented by a tree or a forest, and each example is labeled with a set of classes located on paths from roots to leaves or internal nodes. In other words, both multiple and partial paths are allowed. A straightforward approach to learn a hierarchical classifier, usually used as a baseline method, consists in learning one bin...

An algorithm is presented for fast implementation of time-varying wavelet packets maintaining perfect reconstruction throughout transitions. It is based on filter banks known as the extended lapped transforms in an association following the paths of a binary-tree. Methods for pruning or expanding the branches of the tree are presented and a discussion on adaptation issues is carried. Recently, ...

MOTIVATION A major challenge in gene expression analysis is effective data organization and visualization. One of the most popular tools for this task is hierarchical clustering. Hierarchical clustering allows a user to view relationships in scales ranging from single genes to large sets of genes, while at the same time providing a global view of the expression data. However, hierarchical clust...

Hierarchical clustering is an important technique for hierarchical data exploration applications. However, most existing hierarchial methods are based on traditional one-side clustering, which is not effective for handling high dimensional data. In this paper, we develop a partitional hierarchical co-clustering framework and propose a Hierarchical Information-Theoretical Co-Clustering (HITCC) a...

In most probabilistic topic models, a document is viewed as a collection of tokens and each token is a variable whose values are all the words in a vocabulary. One exception is hierarchical latent tree models (HLTMs), where a document is viewed as a binary vector over the vocabulary and each word is regarded as a binary variable. The use of word variables allows the detection and representation...

Figure 1: Dendrogram with a threshold applied (---) to create clusters. The first cluster has genes A and B while the second one has genes C, D, and E. genes to be measured simultaneously. However, interpretation of the large data sets is complex. Commonly used methods include hierarchical clustering and k-nearest-neighbor [3]. After applying these methods, clusters of co-expressed genes are pr...

While a tracking system is unaware of the identity of any object it tracks, the identity remains the same for the entire tracking sequence. Our system leverages this information by using accumulated joint cooccurrences of the representations within the sequence to create a hierarchical binary-tree classifier of the representations. This classifier is useful to classify sequences as well as indi...

Binary tree encryption (BTE), a relaxation of hierarchical identity-based encryption (HIBE), has recently emerged as a useful and intriguing primitive. On the one hand, the definition of security for BTE is sufficiently “weak” that — in contrast to HIBE — constructions of BTE in the standard model are known. On the other hand, BTE is sufficiently powerful that it yields a number of applications...