7 research outputs found
Random Indexing Re-Hashed
Proceedings of the 18th Nordic Conference of Computational Linguistics
NODALIDA 2011.
Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa.
NEALT Proceedings Series, Vol. 11 (2011), 224-229.
© 2011 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/16955
Drawbacks and Proposed Solutions for Real-time Processing on Existing State-of-the-art Locality Sensitive Hashing Techniques
Nearest-neighbor query processing is a fundamental operation for many image
retrieval applications. Often, images are stored and represented by
high-dimensional vectors that are generated by feature-extraction algorithms.
Since tree-based index structures are shown to be ineffective for high
dimensional processing due to the well-known "Curse of Dimensionality",
approximate nearest neighbor techniques are used for faster query processing.
Locality Sensitive Hashing (LSH) is a very popular and efficient approximate
nearest neighbor technique that is known for its sublinear query processing
complexity and theoretical guarantees. Nowadays, with the emergence of
technology, several diverse application domains require real-time
high-dimensional data storing and processing capacity. Existing LSH techniques
are not suitable to handle real-time data and queries. In this paper, we
discuss the challenges and drawbacks of existing LSH techniques for processing
real-time high-dimensional image data. Additionally, through experimental
analysis, we propose improvements for existing state-of-the-art LSH techniques
for efficient processing of high-dimensional image data.Comment: Accepted and Presented at the 5th International Conference on Signal
and Image Processing (SIGI-2019), Dubai, UA
Intracranial Volume Estimation and Graph Theoretical Analysis of Brain Functional Connectivity Networks
Understanding pathways of neurological disorders requires extensive research on both functional and structural characteristics of the brain. This dissertation introduced two interrelated research endeavors, describing (1) a novel integrated approach for constructing functional connectivity networks (FCNs) of brain using non-invasive scalp EEG recordings; and (2) a decision aid for estimating intracranial volume (ICV). The approach in (1) was developed to study the alterations of networks in patients with pediatric epilepsy. Results demonstrated the existence of statistically significant (
Online generation of locality sensitive hash signatures
Motivated by the recent interest in streaming algorithms for processing large text collections, we revisit the work of Ravichandran et al. (2005) on using the Locality Sensitive Hash (LSH) method of Charikar (2002) to enable fast, approximate comparisons of vector cosine similarity. For the common case of feature updates being additive over a data stream, we show that LSH signatures can be maintained online, without additional approximation error, and with lower memory requirements than when using the standard offline technique.