1 research outputs found
Efficient Similarity Indexing and Searching in High Dimensions
Efficient indexing and searching of high dimensional data has been an area of
active research due to the growing exploitation of high dimensional data and
the vulnerability of traditional search methods to the curse of dimensionality.
This paper presents a new approach for fast and effective searching and
indexing of high dimensional features using random partitions of the feature
space. Experiments on both handwritten digits and 3-D shape descriptors have
shown the proposed algorithm to be highly effective and efficient in indexing
and searching real data sets of several hundred dimensions. We also compare its
performance to that of the state-of-the-art locality sensitive hashing
algorithm