Retrieving relevant text documents on a topic from a large document collection is a challenging task. Different clustering algorithms are developed to retrieve relevant documents of interest. Hierarchical clustering shows quadratic time complexity of O(n 2 ) for n text documents. K-means algorithm has a time complexity of O(n) but it is sensitive to the initial randomly selected cluster centers...

Positioning accuracy with Landmarc localization systems in the literature is around 2 meters. In this study, a reference node topology is introduced and 3-NN weighted algorithm is utilized to determine the unknown target locations. Firstly, target localization is carried out by using simple Landmarc system. Secondly, two stage 3-NN algorithm is deployed to estimate the target location. Finally,...

Citation: Karikari TK, Charway-Felli A, Höglund K, Blennow K and Zetterberg H (2018) Commentary: Global, regional, and national burden of neurological disorders during 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Front. Neurol. 9:201. doi: 10.3389/fneur.2018.00201 Commentary: Global, regional, and national burden of neurological disorders during 1990–2015: a sys...

Recently, the researchers mostly are interested in only the search for the data content that are globally similar to the query, not the search inside data items. This paper presents an algorithm, called \generalized virtual node (GVN)" algorithm, to search for data items where parts of them (sub-datatype) are similar to the incoming query. We call this \sub-datatype"-based multimedia retrieval....

Photon mapping is one of the most important algorithms for computing global illumination. Especially for efficiently producing convincing caustics, there are no real alternatives to photon mapping. On the other hand, photon mapping is also quite costly: Each radiance lookup requires to find the k nearest neighbors in a kd-tree, which can be more costly than shooting several rays. Therefore, the...

Robert Tibshirani Department of Statistics University of Toronto tibs@utstat .toronto.edu Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions We propose a locally adaptive form of nearest neighbor classification to try to finesse this curse of dimensionality. We use a local linear discriminant analysis to e...

We investigate the local stability, prime period-two solutions, boundedness, invariant intervals, and global attractivity of all positive solutions of the following difference equation: yn 1 r pyn yn−k / qyn yn−k , n ∈ N0, where the parameters p, q, r ∈ 0,∞ , k ∈ {1, 2, 3, . . .} and the initial conditions y−k, . . . , y0 ∈ 0,∞ . We show that the unique positive equilibrium of this equation is ...

We discuss one of the shortcomings of the standard K-means algorithm its tendency to converge to a local rather than a global optimum. This is often accommodated by means of different random restarts of the algorithm, however in this paper, we attack the problem by amending the performance function of the algorithm in such a way as to incorporate global information into the performance function...

We present a novel method to transfer speech animation recorded in low resolution videos onto realistic 3D facial models. Unsupervised learning is utilized on a speech video corpus to find underlying manifold of facial configurations. K-means clustering is applied on the low dimensional space to find key speaking-related facial shapes. With a small set of laser scanner captured 3D models relate...