Locality Sensitive Hashing Algorithm Research Articles

Exploring the impact of LSH parameters in privacy-preserving personalization

The “privacy versus personalization” dilemma refers to the situation in which it is necessary for users to disclose their sensitive personal data in order to benefit from collaborative personalized services. Solving this dilemma is a challenge because generating collaborative filtering recommendations requires access to the set of all user profiles in order to identify similar ones, and to compute the top-rated items. The privacy-preserving personalization (P3) paradigm builds on the idea of using locality-sensitive hashing (LSH) to find groups of similar users, while keeping their profiles local. In this work, we analyze the behavior of the adapted LSH algorithm from the perspective of the quality of final recommendations and the distribution of cluster sizes. We investigate the impact of different LSH parameter configurations on the basis of the MovieLens dataset, and empirically show a small, non-prohibitive cost of privacy protection on the recommendations' quality..

A Fast Approximate Nearest Neighbor Search Algorithm in the Hamming Space

A fast approximate nearest neighbor search algorithm for the (binary) Hamming space is proposed. The proposed Error Weighted Hashing (EWH) algorithm is up to 20 times faster than the popular locality sensitive hashing (LSH) algorithm and works well even for large nearest neighbor distances where LSH fails. EWH significantly reduces the number of candidate nearest neighbors by weighing them based on the difference between their hash vectors. EWH can be used for multimedia retrieval and copy detection systems that are based on binary fingerprinting. On a fingerprint database with more than 1,000 videos, for a specific detection accuracy, we demonstrate that EWH is more than 10 times faster than LSH. For the same retrieval time, we show that EWH has a significantly better detection accuracy with a 15 times lower error rate.

Multimedia semantics-aware query-adaptive hashing with bits reconfigurability

In the past decade, locality-sensitive hashing (LSH) has gained a large amount of attention from both the multimedia and computer vision communities owing to its empirical success and theoretic guarantee in large-scale multimedia indexing and retrieval. Original LSH algorithms are designated for generic metrics such as Cosine similarity, $$\ell _2$$ -norm and Jaccard index, which are later extended to support those metrics learned from user-supplied supervision information. One of the common drawbacks of existing algorithms lies in their incapability to be flexibly adapted to the metric changes, along with the inefficacy when handling diverse semantics (e.g., the large number of semantic object categories in the ImageNet database), which motivates our proposed framework toward reconfigurable hashing. The basic idea of the proposed indexing framework is to maintain a large pool of over-complete hashing functions, which are randomly generated and shared when indexing diverse multimedia semantics. For specific semantic category, the algorithm adaptively selects the most relevant hashing bits by maximizing the consistency between semantic distance and hashing-based Hamming distance, thereby achieving reusability of the pre-computed hashing bits. Such a scheme especially benefits the indexing and retrieval of large-scale databases, since it facilitates one-off indexing rather than continuous computation-intensive maintenance toward metric adaptation. In practice, we propose a sequential bit-selection algorithm based on local consistency and global regularization. Extensive studies are conducted on large-scale image benchmarks to comparatively investigate the performance of different strategies for reconfigurable hashing. Despite the vast literature on hashing, to our best knowledge rare endeavors have been spent toward the reusability of hashing structures in large-scale data sets.

A Visual Semantic Concept Detection Algorithm Based on E 2 LSH-MKL

Multiple kernel learning(MKL) methods have a widespread application in visual semantic concept detection.Most canonical MKL approaches employ a linear and stationary kernel combination format which cannot accurately depict complex data distributions.In this paper,we apply exact Euclidean locality sensitive hashing(E2LSH) algorithm to clustering.And by combining the advantages of nonlinear multiple kernel combination methods,we put forward a nonlinear and non-stationary multiple kernel learning method—E2LSH-MKL.In order to make full use of the information generated from the nonlinear interaction of different kernels,this method utilizes Hadamard product to realize nonlinear combination of multiple different kernels.Meanwhile,the method employs E2LSH-based clustering algorithm to group images into sub clusters,then assigns cluster-related kernel weights according to relative contributions of different kernels on each image subset,thereby realizing non-stationary weighting of multiple kernels to improve learning performance;finally,E2LSH-MKL is applied to visual semantic concept detection.Experiment results on datasets of the Caltech256 and the TRECVID 2005 show that the proposed method is superior to the state-of-the-art multiple kernel learning methods.

Approximate Nearest Neighbor Search for a Dataset of Normalized Vectors

This paper describes a novel algorithm for approximate nearest neighbor searching. For solving this problem especially in high dimensional spaces, one of the best-known algorithm is Locality-Sensitive Hashing (LSH). This paper presents a variant of the LSH algorithm that outperforms previously proposed methods when the dataset consists of vectors normalized to unit length, which is often the case in pattern recognition. The LSH scheme is based on a family of hash functions that preserves the locality of points. This paper points out that for our special case problem we can design efficient hash functions that map a point on the hypersphere into the closest vertex of the randomly rotated regular polytope. The computational analysis confirmed that the proposed method could improve the exponent ρ, the main indicator of the performance of the LSH algorithm. The practical experiments also supported the efficiency of our algorithm both in time and in space.

Rapid object indexing using locality sensitive hashing and joint 3D-signature space estimation

We propose a new method for rapid 3D object indexing that combines feature-based methods with coarse alignment-based matching techniques. Our approach achieves a sublinear complexity on the number of models, maintaining at the same time a high degree of performance for real 3D sensed data that is acquired in largely uncontrolled settings. The key component of our method is to first index surface descriptors computed at salient locations from the scene into the whole model database using the Locality Sensitive Hashing (LSH), a probabilistic approximate nearest neighbor method. Progressively complex geometric constraints are subsequently enforced to further prune the initial candidates and eliminate false correspondences due to inaccuracies in the surface descriptors and the errors of the LSH algorithm. The indexed models are selected based on the MAP rule using posterior probability of the models estimated in the joint 3D-signature space. Experiments with real 3D data employing a large database of vehicles, most of them very similar in shape, containing 1,000,000 features from more than 365 models demonstrate a high degree of performance in the presence of occlusion and obscuration, unmodeled vehicle interiors and part articulations, with an average processing time between 50 and 100 seconds per query.

Scalable Partitioning and Exploration of Chemical Spaces Using Geometric Hashing

Virtual screening (VS) has become a preferred tool to augment high-throughput screening(1) and determine new leads in the drug discovery process. The core of a VS informatics pipeline includes several data mining algorithms that work on huge databases of chemical compounds containing millions of molecular structures and their associated data. Thus, scaling traditional applications such as classification, partitioning, and outlier detection for huge chemical data sets without a significant loss in accuracy is very important. In this paper, we introduce a data mining framework built on top of a recently developed fast approximate nearest-neighbor-finding algorithm(2) called locality-sensitive hashing (LSH) that can be used to mine huge chemical spaces in a scalable fashion using very modest computational resources. The core LSH algorithm hashes chemical descriptors so that points close to each other in the descriptor space are also close to each other in the hashed space. Using this data structure, one can perform approximate nearest-neighbor searches very quickly, in sublinear time. We validate the accuracy and performance of our framework on three real data sets of sizes ranging from 4337 to 249 071 molecules. Results indicate that the identification of nearest neighbors using the LSH algorithm is at least 2 orders of magnitude faster than the traditional k-nearest-neighbor method and is over 94% accurate for most query parameters. Furthermore, when viewed as a data-partitioning procedure, the LSH algorithm lends itself to easy parallelization of nearest-neighbor classification or regression. We also apply our framework to detect outlying (diverse) compounds in a given chemical space; this algorithm is extremely rapid in determining whether a compound is located in a sparse region of chemical space or not, and it is quite accurate when compared to results obtained using principal-component-analysis-based heuristics.