Spectral manifold learning techniques have recently found extensive applications in machine vision. The common strategy of spectral algorithms for manifold learning is exploiting the local relationships in a symmetric adjacency graph, which is typically constructed using -nearest neighborhood ( -NN) criterion. In this paper, with our focus on locally linear embedding as a powerful and well-know...

Collaborative filtering (CF) is the most widely used and successful approach for personalized service recommendations. Among the collaborative recommendation approaches, neighborhood based approaches enjoy a huge amount of popularity, due to their simplicity, justifiability, efficiency and stability. Neighborhood based collaborative filtering approach finds K nearest neighbors to an active user...

BACKGROUND We examined the relationship between overweight in preschool children and three environmental factors--the proximity of the children's residences to playgrounds and to fast food restaurants and the safety of the children's neighborhoods. We hypothesized that children who lived farther from playgrounds, closer to fast food restaurants, and in unsafe neighborhoods were more likely to b...

A novel solution is presented to the Nearest Neighbor Problem that is specifically tailored for determining correspondences within the Iterative Closest Point Algorithm. The reference point set P is preprocessed by calculating for each point ~pi 2 P that neighborhood of points which lie within a certain distance of ~pi. The points within each -neighborhood are sorted by increasing distance to t...

Time-frequency analysis is performed for chaotic flow with a power spectrum estimator based on the phase-space neighborhood. The relation between the reference phase point and its nearest neighbors is demonstrated. The nearest neighbors, representing the state recurrences in the phase space reconstructed by time delay embedding, actually cover data segments with similar wave forms and thus poss...

Consider a set of N objects, consisting of Ni objects originating from population Ai, i = 1,. . . , k, so that N = Zf-, Ni. In order to classify the set of N objects, we measure a variable with pdf A for objects originating from population Ai. Let x1,x2;. .,x, be the measured values of the N objects, which are assumed to be independent. Now consider the N-tuple Gi = (gljt g2j, * * ’ 3 Sp ’ ’ ’ ...

BACKGROUND Understanding the evolutionary relationships among species based on their genetic information is one of the primary objectives in phylogenetic analysis. Reconstructing phylogenies for large data sets is still a challenging task in Bioinformatics. RESULTS We propose a new distance-based clustering method, the shortest triplet clustering algorithm (STC), to reconstruct phylogenies. T...

Abstract We propose a novel framework for cross- lingual content flagging with limited target- language data, which significantly outperforms prior work in terms of predictive performance. The is based on nearest-neighbor architecture. It modern instantiation the vanilla k-nearest neighbor model, as we use Transformer representations all its components. Our can adapt to new source- instances, w...

We introduce the consistent topological structure and neighborhood structure into the logical framework for providing the logical foundation and logical normalization for the approximate reasoning. We present the concept of the formulae mass, theknowledge mass and the approximating knowledge closure of the knowledge library by means of topological closure. We obtain the fundamental framework of...