6,112 research outputs found
FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search
We present FLASH (\textbf{F}ast \textbf{L}SH \textbf{A}lgorithm for
\textbf{S}imilarity search accelerated with \textbf{H}PC), a similarity search
system for ultra-high dimensional datasets on a single machine, that does not
require similarity computations and is tailored for high-performance computing
platforms. By leveraging a LSH style randomized indexing procedure and
combining it with several principled techniques, such as reservoir sampling,
recent advances in one-pass minwise hashing, and count based estimations, we
reduce the computational and parallelization costs of similarity search, while
retaining sound theoretical guarantees.
We evaluate FLASH on several real, high-dimensional datasets from different
domains, including text, malicious URL, click-through prediction, social
networks, etc. Our experiments shed new light on the difficulties associated
with datasets having several million dimensions. Current state-of-the-art
implementations either fail on the presented scale or are orders of magnitude
slower than FLASH. FLASH is capable of computing an approximate k-NN graph,
from scratch, over the full webspam dataset (1.3 billion nonzeros) in less than
10 seconds. Computing a full k-NN graph in less than 10 seconds on the webspam
dataset, using brute-force (), will require at least 20 teraflops. We
provide CPU and GPU implementations of FLASH for replicability of our results
Efficient end-to-end learning for quantizable representations
Embedding representation learning via neural networks is at the core
foundation of modern similarity based search. While much effort has been put in
developing algorithms for learning binary hamming code representations for
search efficiency, this still requires a linear scan of the entire dataset per
each query and trades off the search accuracy through binarization. To this
end, we consider the problem of directly learning a quantizable embedding
representation and the sparse binary hash code end-to-end which can be used to
construct an efficient hash table not only providing significant search
reduction in the number of data but also achieving the state of the art search
accuracy outperforming previous state of the art deep metric learning methods.
We also show that finding the optimal sparse binary hash code in a mini-batch
can be computed exactly in polynomial time by solving a minimum cost flow
problem. Our results on Cifar-100 and on ImageNet datasets show the state of
the art search accuracy in precision@k and NMI metrics while providing up to
98X and 478X search speedup respectively over exhaustive linear search. The
source code is available at
https://github.com/maestrojeong/Deep-Hash-Table-ICML18Comment: Accepted and to appear at ICML 2018. Camera ready versio
An intelligent information forwarder for healthcare big data systems with distributed wearable sensors
© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed
Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases
In this paper we present an efficient method for visual descriptors retrieval
based on compact hash codes computed using a multiple k-means assignment. The
method has been applied to the problem of approximate nearest neighbor (ANN)
search of local and global visual content descriptors, and it has been tested
on different datasets: three large scale public datasets of up to one billion
descriptors (BIGANN) and, supported by recent progress in convolutional neural
networks (CNNs), also on the CIFAR-10 and MNIST datasets. Experimental results
show that, despite its simplicity, the proposed method obtains a very high
performance that makes it superior to more complex state-of-the-art methods
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
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