TopSig: Topology Preserving Document Signatures

Performance comparisons between File Signatures and Inverted Files for text retrieval have previously shown several significant shortcomings of file signatures relative to inverted files. The inverted file approach underpins most state-of-the-art search engine algorithms, such as Language and Probabilistic models. It has been widely accepted that traditional file signatures are inferior alternatives to inverted files. This paper describes TopSig, a new approach to the construction of file signatures. Many advances in semantic hashing and dimensionality reduction have been made in recent times, but these were not so far linked to general purpose, signature file based, search engines. This paper introduces a different signature file approach that builds upon and extends these recent advances. We are able to demonstrate significant improvements in the performance of signature file based indexing and retrieval, performance that is comparable to that of state of the art inverted file based systems, including Language models and BM25. These findings suggest that file signatures offer a viable alternative to inverted files in suitable settings and from the theoretical perspective it positions the file signatures model in the class of Vector Space retrieval models.Comment: 12 pages, 8 figures, CIKM 201

Learning compact hashing codes with complex objectives from multiple sources for large scale similarity search

Similarity search is a key problem in many real world applications including image and text retrieval, content reuse detection and collaborative filtering. The purpose of similarity search is to identify similar data examples given a query example. Due to the explosive growth of the Internet, a huge amount of data such as texts, images and videos has been generated, which indicates that efficient large scale similarity search becomes more important.^ Hashing methods have become popular for large scale similarity search due to their computational and memory efficiency. These hashing methods design compact binary codes to represent data examples so that similar examples are mapped into similar codes. This dissertation addresses five major problems for utilizing supervised information from multiple sources in hashing with respect to different objectives. Firstly, we address the problem of incorporating semantic tags by modeling the latent correlations between tags and data examples. More precisely, the hashing codes are learned in a unified semi-supervised framework by simultaneously preserving the similarities between data examples and ensuring the tag consistency via a latent factor model. Secondly, we solve the missing data problem by latent subspace learning from multiple sources. The hashing codes are learned by enforcing the data consistency among different sources. Thirdly, we address the problem of hashing on structured data by graph learning. A weighted graph is constructed based on the structured knowledge from the data. The hashing codes are then learned by preserving the graph similarities. Fourthly, we address the problem of learning high ranking quality hashing codes by utilizing the relevance judgments from users. The hashing code/function is learned via optimizing a commonly used non-smooth non-convex ranking measure, NDCG. Finally, we deal with the problem of insufficient supervision by active learning. We propose to actively select the most informative data examples and tags in a joint manner based on the selection criteria that both the data examples and tags should be most uncertain and dissimilar with each other.^ Extensive experiments on several large scale datasets demonstrate the superior performance of the proposed approaches over several state-of-the-art hashing methods from different perspectives

Signature file access methodologies for text retrieval: a literature review with additional test cases

Signature files are extremely compressed versions of text files which can be used as access or index files to facilitate searching documents for text strings. These access files, or signatures, are generated by storing hashed codes for individual words. Given the possible generation of similar codes in the hashing or storing process, the primary concern in researching signature files is to determine the accuracy of retrieving information. Inaccuracy is always represented by the false signaling of the presence of a text string. Two suggested ways to alter false drop rates are: 1) to determine if either of the two methologies for storing hashed codes, by superimposing them or by concatenating them, is more efficient; and 2) to determine if a particular hashing algorithm has any impact. To assess these issues, the history of suprimposed coding is traced from its development as a tool for compressing information onto punched cards in the 1950s to its incorporation into proposed signature file methodologies in the mid-1980\u27 s. Likewise, the concept of compressing individual words by various algorithms, or by hashing them is traced through the research literature. Following this literature review, benchmark trials are performed using both superimposed and concatenated methodologies while varying hashing algorithms. It is determined that while one combination of hashing algorithm and storage methodology is better, all signature file mehods can be considered viable

Spectral Approaches to Nearest Neighbor Search

We study spectral algorithms for the high-dimensional Nearest Neighbor Search problem (NNS). In particular, we consider a semi-random setting where a dataset $P$ in $\mathbb{R}^d$ is chosen arbitrarily from an unknown subspace of low dimension $k\ll d$, and then perturbed by fully $d$-dimensional Gaussian noise. We design spectral NNS algorithms whose query time depends polynomially on $d$ and $\log n$ (where $n=|P|$) for large ranges of $k$, $d$ and $n$. Our algorithms use a repeated computation of the top PCA vector/subspace, and are effective even when the random-noise magnitude is {\em much larger} than the interpoint distances in $P$. Our motivation is that in practice, a number of spectral NNS algorithms outperform the random-projection methods that seem otherwise theoretically optimal on worst case datasets. In this paper we aim to provide theoretical justification for this disparity.Comment: Accepted in the proceedings of FOCS 2014. 30 pages and 4 figure