2,709 research outputs found
Efficient similarity search in high-dimensional data spaces
Similarity search in high-dimensional data spaces is a popular paradigm for many modern database applications, such as content based image retrieval, time series analysis in financial and marketing databases, and data mining. Objects are represented as high-dimensional points or vectors based on their important features. Object similarity is then measured by the distance between feature vectors and similarity search is implemented via range queries or k-Nearest Neighbor (k-NN) queries.
Implementing k-NN queries via a sequential scan of large tables of feature vectors is computationally expensive. Building multi-dimensional indexes on the feature vectors for k-NN search also tends to be unsatisfactory when the dimensionality is high. This is due to the poor index performance caused by the dimensionality curse.
Dimensionality reduction using the Singular Value Decomposition method is the approach adopted in this study to deal with high-dimensional data. Noting that for many real-world datasets, data distribution tends to be heterogeneous, dimensionality reduction on the entire dataset may cause a significant loss of information. More efficient representation is sought by clustering the data into homogeneous subsets of points, and applying dimensionality reduction to each cluster respectively, i.e., utilizing local rather than global dimensionality reduction.
The thesis deals with the improvement of the efficiency of query processing associated with local dimensionality reduction methods, such as the Clustering and Singular Value Decomposition (CSVD) and the Local Dimensionality Reduction (LDR) methods. Variations in the implementation of CSVD are considered and the two methods are compared from the viewpoint of the compression ratio, CPU time, and retrieval efficiency.
An exact k-NN algorithm is presented for local dimensionality reduction methods by extending an existing multi-step k-NN search algorithm, which is designed for global dimensionality reduction. Experimental results show that the new method requires less CPU time than the approximate method proposed original for CSVD at a comparable level of accuracy.
Optimal subspace dimensionality reduction has the intent of minimizing total query cost. The problem is complicated in that each cluster can retain a different number of dimensions. A hybrid method is presented, combining the best features of the CSVD and LDR methods, to find optimal subspace dimensionalities for clusters generated by local dimensionality reduction methods. The experiments show that the proposed method works well for both real-world datasets and synthetic datasets
Scalable Image Retrieval by Sparse Product Quantization
Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional
feature indexing and retrieval is the crux of large-scale image retrieval. A
recent promising technique is Product Quantization, which attempts to index
high-dimensional image features by decomposing the feature space into a
Cartesian product of low dimensional subspaces and quantizing each of them
separately. Despite the promising results reported, their quantization approach
follows the typical hard assignment of traditional quantization methods, which
may result in large quantization errors and thus inferior search performance.
Unlike the existing approaches, in this paper, we propose a novel approach
called Sparse Product Quantization (SPQ) to encoding the high-dimensional
feature vectors into sparse representation. We optimize the sparse
representations of the feature vectors by minimizing their quantization errors,
making the resulting representation is essentially close to the original data
in practice. Experiments show that the proposed SPQ technique is not only able
to compress data, but also an effective encoding technique. We obtain
state-of-the-art results for ANN search on four public image datasets and the
promising results of content-based image retrieval further validate the
efficacy of our proposed method.Comment: 12 page
Hashing for Similarity Search: A Survey
Similarity search (nearest neighbor search) is a problem of pursuing the data
items whose distances to a query item are the smallest from a large database.
Various methods have been developed to address this problem, and recently a lot
of efforts have been devoted to approximate search. In this paper, we present a
survey on one of the main solutions, hashing, which has been widely studied
since the pioneering work locality sensitive hashing. We divide the hashing
algorithms two main categories: locality sensitive hashing, which designs hash
functions without exploring the data distribution and learning to hash, which
learns hash functions according the data distribution, and review them from
various aspects, including hash function design and distance measure and search
scheme in the hash coding space
- …