Modeling LSH for performance tuning

Dong, Wei; Wang, Zhe; Josephson, William; Charikar, Moses; Li, Kai

doi:10.1145/1458082.1458172

Cited by 113 publications

(87 citation statements)

References 20 publications

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“…Donga et. al [27] have proposed an approach for performance tuning of LSH. The algorithm can be easily parallelized, because the different operations related to computing the distance function offer concurrency.…”

Section: Resultsmentioning

confidence: 99%

Efficient Clustering of Metagenomic Sequences using Locality Sensitive Hashing

Rasheed

Rangwala

Barbará

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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The new generation of genomic technologies have allowed researchers to determine the collective DNA of organisms (e.g., microbes) co-existing as communities across the ecosystem (e.g., within the human host). There is a need for the computational approaches to analyze and annotate the large volumes of available sequence data from such microbial communities (metagenomes).In this paper, we developed an efficient and accurate metagenome clustering approach that uses the locality sensitive hashing (LSH) technique to approximate the computational complexity associated with comparing sequences. We introduce the use of fixed-length, gapless subsequences for improving the sensitivity of the LSH-based similarity function. We evaluate the performance of our algorithm on two metagenome datasets associated with microbes existing across different human skin locations. Our empirical results show the strength of the developed approach in comparison to three state-of-the-art sequence clustering algorithms with regards to computational efficiency and clustering quality. We also demonstrate practical significance for the developed clustering algorithm, to compare bacterial diversity and structure across different skin locations.

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Section: Resultsmentioning

confidence: 99%

Efficient Clustering of Metagenomic Sequences using Locality Sensitive Hashing

Rasheed

Rangwala

Barbará

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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“…the hash family H) depends on the parameter R, which is an estimate of distance between a normal po int and one of its neighbours. There exists several research efforts focusing on addressing issues related to LSH parameter tuning [2] [7]. The LSH-based outlier detection [20] relies on ranking for efficiency, not correctness.…”

Section: Outlier Likelihood Rankingmentioning

confidence: 99%

RODHA: Robust Outlier Detection using Hybrid Approach

Mira¹,

Bhattacharyya²,

Saharia³

2012

AJIS

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The task of outlier detection is to find the small groups of data objects that are exceptional to the inherent behavior of the rest of the data. Detection of such outliers is fundamental to a variety of database and analytic tasks such as fraud detection and customer migration. There are several approaches [10] of outlier detection employed in many study areas amongst which distance based and density based outlier detection techniques have gathered most attention of researchers. In informat ion theory, entropy is a core concept that measures uncertainty about a stochastic event, and it means that entropy describes the distribution of an event. Because of its ability to describe the distribution of data, entropy has been applied in clustering applications in data mining. In this paper, we have developed a robust supervised outlier detection algorith m using hybrid approach (RODHA) which incorporates both the concept of distance and density along with entropy measure while determining an outlier. We have provided an empirical study of different existing outlier detection algorithms and established the effectiveness of the proposed RODHA in co mparison to other outlier detection algorith ms.

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“…ing (LSH) [6], a popular and effective high-dimensional indexing technique for approximate nearest neighbor search.…”

Section: Search-based Face Annotation Frameworkmentioning

confidence: 99%

Mining weakly labeled web facial images for search-based face annotation

Wang

Hoi

2011

Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

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In this paper, we investigate a search-based face annotation framework by mining weakly labeled facial images that are freely available on the internet. A key component of such a search-based annotation paradigm is to build a database of facial images with accurate labels. This is however challenging since facial images on the WWW are often noisy and incomplete. To improve the label quality of raw web facial images, we propose an effective Unsupervised Label Refinement (ULR) approach for refining the labels of web facial images by exploring machine learning techniques. We develop effective optimization algorithms to solve the large-scale learning tasks efficiently, and conduct an extensive empirical study on a web facial image database with 400 persons and 40,000 web facial images. Encouraging results showed that the proposed ULR technique can significantly boost the performance of the promising search-based face annotation scheme.

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Modeling LSH for performance tuning

Cited by 113 publications

References 20 publications

Efficient Clustering of Metagenomic Sequences using Locality Sensitive Hashing

Efficient Clustering of Metagenomic Sequences using Locality Sensitive Hashing

RODHA: Robust Outlier Detection using Hybrid Approach

Mining weakly labeled web facial images for search-based face annotation

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