Similarity search is a fundamental problem in computing science with various applications and has attracted significant research attention, especially in large-scale search with high dimensions. Motivated by the evidence in biological science, our work develops a novel approach for similarity search. Fundamentally different from existing methods that typically reduce the dimension of the data to lessen the computational complexity and speed up the search, our approach projects the data into an even higher-dimensional space while ensuring the sparsity of the data in the output space, with the objective of further improving precision and speed. Specifically, our approach has two key steps. Firstly, it computes the optimal sparse lifting for given input samples and increases the dimension of the data while approximately preserving their pairwise similarity. Secondly, it seeks the optimal lifting operator that best maps input samples to the optimal sparse lifting. Computationally, both steps are modeled as optimization problems that can be efficiently and effectively solved by the Frank-Wolfe algorithm. Simple as it is, our approach has reported significantly improved results in empirical evaluations, and exhibited its high potentials in solving practical problems.

Neural Information Processing Systems http://nips.cc/

Similarity search is a fundamental problem in computing science with various applications and has attracted significant research attention, especially in large-scale search with high dimensions. Motivated by the evidence in biological science, our work develops a novel approach for similarity search. Fundamentally different from existing methods that typically reduce the dimension of the data to lessen the computational complexity and speed up the search, our approach projects the data into an even higher-dimensional space while ensuring the sparsity of the data in the output space, with the objective of further improving precision and speed. Specifically, our approach has two key steps. Firstly, it computes the optimal sparse lifting for given input samples and increases the dimension of the data while approximately preserving their pairwise similarity. Secondly, it seeks the optimal lifting operator that best maps input samples to the optimal sparse lifting. Computationally, both steps are modeled as optimization problems that can be efficiently and effectively solved by the Frank-Wolfe algorithm. Simple as it is, our approach has reported significantly improved results in empirical evaluations, and exhibited its high potentials in solving practical problems.

Similarity search is a fundamental problem in computing science with various applications and has attracted significant research attention, especially in large-scale search with high dimensions.

Hashing is one of the most popular methods for similarity search because of its speed and efficiency. Dense binary hashing is prevalent in the literature. Recently, insect olfaction was shown to be structurally and functionally analogous to sparse hashing [6]. Here, we prove that this biological mechanism is the solution to a well-posed optimization problem. Furthermore, we show that orthogonality increases the accuracy of sparse hashing. Next, we present a novel method, Procrustean Orthogonal Sparse Hashing (POSH), that unifies these findings, learning an orthogonal transform from training data compatible with the sparse hashing mechanism. We provide theoretical evidence of the shortcomings of Optimal Sparse Lifting (OSL) [22] and BioHash [30], two related olfaction-inspired methods, and propose two new methods, Binary OSL and SphericalHash, to address these deficiencies. We compare POSH, Binary OSL, and SphericalHash to several state-of-the-art hashing methods and provide empirical results for the superiority of the proposed methods across a wide range of standard benchmarks and parameter settings.

Similarity search is a fundamental problem in computing science with various applications and has attracted significant research attention, especially in large-scale search with high dimensions. Motivated by the evidence in biological science, our work develops a novel approach for similarity search. Fundamentally different from existing methods that typically reduce the dimension of the data to lessen the computational complexity and speed up the search, our approach projects the data into an even higher-dimensional space while ensuring the sparsity of the data in the output space, with the objective of further improving precision and speed. Specifically, our approach has two key steps. Firstly, it computes the optimal sparse lifting for given input samples and increases the dimension of the data while approximately preserving their pairwise similarity. Secondly, it seeks the optimal lifting operator that best maps input samples to the optimal sparse lifting. Computationally, both steps are modeled as optimization problems that can be efficiently and effectively solved by the Frank-Wolfe algorithm. Simple as it is, our approach has reported significantly improved results in empirical evaluations, and exhibited its high potentials in solving practical problems.

Similarity search is a fundamental problem in computing science with various applications and has attracted significant research attention, especially in large-scale search with high dimensions. Motivated by the evidence in biological science, our work develops a novel approach for similarity search. Fundamentally different from existing methods that typically reduce the dimension of the data to lessen the computational complexity and speed up the search, our approach projects the data into an even higher-dimensional space while ensuring the sparsity of the data in the output space, with the objective of further improving precision and speed. Specifically, our approach has two key steps. Firstly, it computes the optimal sparse lifting for given input samples and increases the dimension of the data while approximately preserving their pairwise similarity. Secondly, it seeks the optimal lifting operator that best maps input samples to the optimal sparse lifting. Computationally, both steps are modeled as optimization problems that can be efficiently and effectively solved by the Frank-Wolfe algorithm. Simple as it is, our approach has reported significantly improved results in empirical evaluations, and exhibited its high potentials in solving practical problems.