Abstract. Nearest neighbor search has a wide variety of applications. Unfortunately, the majority of search methods do not scale well with dimensionality. Recent efforts have been ...
The "nearest neighbor" relation, or more generally the "k nearest neighbors" relation, defined for a set of points in a metric space, has found many uses in co...
In the k-nearest neighbor (KNN) classifier, nearest neighbors involve only labeled data. That makes it inappropriate for the data set that includes very few labeled data. In this ...
Similarity search methods are widely used as kernels in various data mining and machine learning applications including those in computational biology, web search/clustering. Near...
Abstract. PatchMatch is a fast algorithm for computing dense approximate nearest neighbor correspondences between patches of two image regions [1]. This paper generalizes PatchMatc...
Locally Linear Embedding (LLE) has recently been proposed as a method for dimensional reduction of high-dimensional nonlinear data sets. In LLE each data point is reconstructed fro...
Claudio Varini, Andreas Degenhard, Tim W. Nattkemp...
Organizing digital images into semantic categories is imperative for effective browsing and retrieval. In large image collections, an efficient algorithm is crucial to quickly cat...
Taufik Abidin, Aijuan Dong, Honglin Li, William Pe...
The M-tree and its variants have been proved to provide an efficient similarity search in database environments. In order to further improve their performance, in this paper we pro...
Searching approximate nearest neighbors in large scale high dimensional data set has been a challenging problem. This paper presents a novel and fast algorithm for learning binary...