Document indexing and representation of term-document relations are very important for document clustering and retrieval. In this paper, we combine a graph-based dimensionality reduction method with a corpus-based association measure within the Generalized Latent Semantic Analysis framework. We evaluate the graph-based GLSA on the document clustering task.

We proposed a new method to select distinctive features for 3D shape. This approach combines the techniques of neighborhood preserved dimensionality reduction algorithm with clustering method. Based on experimental result, it turns out that the proposed method has better distinctive feature retrieval performance in low dimensional mapping. Using our method, we achieved a reasonably accuracy in ...

For ℓ 2 , there exists the powerful dimension reduction transform of Johnson and Lindenstrauss [JL84], with a host of known applications. Here, we consider the problem of dimension reduction for all ℓ p spaces 1 ≤ p < ∞. Although strong lower bounds are known for dimension reduction in ℓ 1 , Ostrovsky and Rabani [OR02] successfully circumvented these by presenting an ℓ 1 embedding that maintain...

A stochastic-optimization technique based on time series cluster analysis is described for Index Tracking and Enhanced Index Tracking problems. Our methodology solves the problem in two steps, i.e., by first selecting a subset of stocks and then setting the weight of each stock as a result of an optimization process (asset allocation). Present formulation takes into account constraints on the n...

The spatial dependence structure of climate extremes may be represented by the class of max-stable distributions. When the domain is very large, describing the spatial dependence between and within subdomains is particularly challenging and requires very flexible, yet interpretable, models. In this work, we use the inherent hierarchical dependence structure of the (max-stable) nested logistic d...

This paper describes a modification of a possibilistic clustering method based on the concept of allotment among fuzzy clusters. Basic ideas of the method are considered and the concept of a principal allotment among fuzzy clusters is introduced. The paper provides the description of the plan of the algorithm for detection principal allotment. An analysis of experimental results of the proposed...

Cluster analysis is a fundamental technique in pattern recognition. It is difficult to cluster data on complex data sets. This paper presents a new algorithm for clustering. There are three key ideas in the algorithm: using mutual neighborhood graphs to discover knowledge and cluster data; using eigenvalues of local covariance matrixes to express knowledge and form a knowledge embedded space; a...

Different document representation models have been proposed to measure semantic similarity between documents using corpus statistics. Some of these models explicitly estimate semantic similarity based on measures of correlations between terms, while others apply dimension reduction techniques to obtain latent representation of concepts. This paper proposes new hybrid models that combine explici...

In this paper we propose a new approach for learning a summarized representation of high dimensional continuous data. We apply the model to learn efficient representations of grasp data for two robotic scenarios that facilitates a compact summarization. Our technique consists of a Bayesian non-parametric model capable of encoding highdimensional data from complex distributions using a sparse su...

This paper discusses one method of clustering a high dimensional dataset using dimensionality reduction and context dependency measures (CDM). First, the dataset is partitioned into a predefined number of clusters using CDM. Then, context dependency measures are combined with several dimensionality reduction techniques and for each choice the data set is clustered again. The results are combine...