In this letter, we present a novel method for reconstructing continuous data field from scattered point data, which leads to a more characteristic visualization result by volume rendering. The gradient distribution of scattered point data is analyzed for local feature investigation via singular-value decomposition. A data-adaptive ellipsoidal shaped function is constructed as the penalty functi...

In this paper we present a novel non-parametric method of simplifying piecewise linear curves and we apply this method as a statistical approximation of structure within sequential data in the plane. We consider the problem of minimizing the average length of sequences of consecutive input points that lie on any one side of the simplified curve. Specifically, given a sequence P of n points in t...

We discuss the problem of extending data mining approaches to cases in which data points arise in the form of individual graphs. Being able to find the intrinsic low-dimensionality in ensembles of graphs can be useful in a variety of modeling contexts, especially when coarse-graining the detailed graph information is of interest. One of the main challenges in mining graph data is the definition...

The general problem of 3D shape reconstruction from unorganized data sets can be described as follows: Given 3D range data or dense 3D scattered point sets sampled on or near an unknown object surface, reconstruct the topological and geometrical structures represented by the data points. The data points should be approximated by a set of smooth, connected surface patches such that the accuracy ...

Figure 2: Trees drawn from the prior of the nCRP (top) and TSSB (bottom) models with N = 100 data points. In both cases we used a hyper-parameter of γ = 1. For TSSB, we further set α = 10 and λ = 2 (these are parameters that do not exist in the nCRP). Note that the tree generated by TSSB is very wide and shallow. A larger value of α would fix this for N = 100, but increasing N would cause the p...

A SOM based model combination strategy, allowing to create adaptive—data dependent—committees, is proposed. Both, models included into a committee and aggregation weights are specific for each input data point analyzed. The possibility to detect outliers is one more characteristic feature of the strategy.