Image Steganography using Combined Nearest and Farthest Neighbors Methods

Feature Selection for Clustering by Exploring Nearest and Farthest Neighbors

Feature selection has been explored extensively for use in several real-world applications. In this paper, we propose a new method to select a salient subset of features from unlabeled data, and the selected features are then adaptively used to identify natural clusters in the cluster analysis. Unlike previous methods that select salient features for clustering, our method does not require a pr...

Feature selection for clustering using instance-based learning by exploring the nearest and farthest neighbors

Naive Bayes Image Classification: Beyond Nearest Neighbors

Naive Bayes Nearest Neighbor (NBNN) has been proposed as a powerful, learning-free, non-parametric approach for object classification. Its good performance is mainly due to the avoidance of a vector quantization step, and the use of image-to-class comparisons, yielding good generalization. In this paper we study the replacement of the nearest neighbor part with more elaborate and robust (sparse...

Nearest neighbors methods for support vector machines

An important part of Pattern Recognition deals with the problem of classification of data into a finite number of categories. In the usual setting of " supervised learning " , examples are given that consists of pairs, (X i , Y i), i ≤ n, where X i is the d-dimensional covariate vector and y i is the corresponding " category " in some finite set C. In the examples, y i is known! Based on these ...

On Top-$k$ Weighted SUM Aggregate Nearest and Farthest Neighbors in the $L_1$ Plane

In this paper, we present algorithms for the top-k nearest neighbor searching where the input points are exact and the query point is uncertain under the L1 metric in the plane. The uncertain query point is represented by a discrete probability distribution function, and the goal is to efficiently return the top-k expected nearest neighbors, which have the smallest expected distances to the que...