Kazuo Aoyama

Fast approximate similarity search based on degree-reduced neighborhood graphs

This paper presents a fast approximate similarity search method for finding the most similar object to a given query object from an object set with a dissimilarity with a success probability exceeding a given value. As a search index, the proposed method utilizes a degree-reduced k-nearest neighbor (k-DR) graph constructed from the object set with the dissimilarity, and explores the k-DR graph along its edges using a greedy search (GS) algorithm starting from multiple initial vertices with parallel processing. In the graph-construction stage, the structural parameter k of the k-DR graph is determined so that the probability with which at least one search trial of those with multiple initial vertices succeeds is more than the given success probability. To estimate the greedy-search success probability, we introduce the concept of a basin in the k-DR graph. The experimental results on a real data set verify the approximation scheme and high search performance of the proposed method and demonstrate that it is superior to E2LSH in terms of the expected search cost.

A Threshold Logic-Based Reconfigurable Logic Element with a New Programming Technology

We introduce a new reconfigurable logic element. Its operations are based on threshold logic and it can store its own configuration data. The element is composed of threshold gates which are implemented in a two-level feed-forward neuron MOS circuit. We present a effective method of switching and storing the values of two variable thresholds in a neuron MOS transistor so as to reconfigure the element without requiring any additional memory circuit or device. Circuit simulation results are provided to verify the correct operations of the reconfigurable element in the following four function modes: symmetric function modes with/without configuration data storage, and multiplexer function modes with/without control data storage.

Fast Similarity Search in Small-World Networks

We present a novel graph-based approach for fast similarity searches suitable for large-scale and high-dimensional data sets. We focus on a well-known feature of small-world networks, they are “searchable,” and propose an efficient index structure called a degree-reduced nearest neighbor graph. A similarity search is then formulated as a problem of finding the most similar object to a query object by following the links in this graph with a best-first neighborhood search algorithm. The experimental results show that the proposed search method significantly reduces search costs. In particular, we apply it to data sets consisting of nearly one million documents, and successfully reduce the average number of similarity evaluations to only 0.9% of the total number of documents.

Fast similarity search on a large speech data set with neighborhood graph indexing

This paper presents a novel graph-based approach for solving a problem of fast finding a speech model acoustically similar to a query model from a large set of speech models. Each speech model in the set is represented by a Gaussian mixture model and dissimilarity from a GMM to another is measured with a Kullback-Leibler divergence (KLD). Conventional pruning techniques based on the triangle inequality for fast similarity search are not available because the model space with a KLD is not a metric space. We propose a search method that is characterized by an index of a degree-reduced nearest neighbor (DRNN) graph. The search method can efficiently find the most similar (closest) GMM to a query, exploring the DRNN graph with a best-first manner. Experimental evaluations on utterance GMM search tasks reveal a significantly low computational cost of the proposed method.

Zero-resource spoken term detection using hierarchical graph-based similarity search

This paper presents fast zero-resource spoken term detection (STD) in a large-scale data set, by using a hierarchical graph-based similarity search method (HGSS). HGSS is an improved graph-based similarity search method (GSS) in terms of a search space for high-speed performance. Instead of a degree-reduced k-nearest neighbor (k-DR) graph for GSS, a hierarchical k-DR graph, which is constructed based on a cluster structure in the corresponding k-DR graph, is used as an index for HGSS. A search algorithm for the hierarchical k-DR graph effectively utilizes the cluster structure, resulting in the reduction of the search space. HGSS inherits the useful property of GSS; it is available for any data sets without limits on a data type nor a defined dissimilarity since a graph is a general expression of a relationship between objects. A vertex and an edge in the hierarchical graph correspond to a Gaussian mixture model (GMM) posterior-gram segment and the relationship between a pair of GMM poste-riorgram segments, which is measured by dynamic time warping, respectively. Experimental results demonstrate that HGSS successfully reduces the computational cost by more than 40 % at nearly the same accuracy, compared to GSS.

Graph index based query-by-example search on a large speech data set

This paper presents a neighborhood graph index approach for query-by-example search using dynamic time warping (DTW) on Gaussian mixture model (GMM) posteriorgram sequences. The approach is intended to achieve a significant speed-up of a spoken term detection (STD) task for resource-limited situations. The proposed method employs a degree-reduced k-nearest neighbor (k-DR) graph as an index. A set of k-DR graphs is pre-constructed off-line from a large number of GMM posteriorgram sequences. Given a query posteriorgram sequence, one k-DR graph is selected from the set as the index. By applying a newly introduced combination of greedy-search (GS) and breadth-first search (BFS) algorithms to the selected k-DR graph index, the proposed method efficiently achieves query-by-example STD. Experimental results on the MIT lecture corpus demonstrate that the proposed method works much faster than a state-of-art method by more than one order magnitude, keeping almost the same precision.

Threshold Element-Based Symmetric Function Generators and Their Functional Extension

This paper presents threshold element-based symmetric function generators (SFG) and techniques for their functional extension. Any symmetric function of k input variables is realized by SFGs with (k+ 1) configuration bits. When less than k input variables are applied to the SFGs, certain logic functions beyond symmetric functions can be achieved by configurations of interconnection resources. Logic functions achieved by the extended SFGs are compared with those by SRAMbased look-up tables. It is demonstrated that the SFGs have flexibility in achievable function classes and utilize configuration bits economically.

Pivot-based k-means Algorithm for Numerous-class Data Sets.

This paper presents an accelerated k-means clustering algorithm suitable for a large-scale and numerous-class data set. The proposed iterative algorithm avoids unnecessary exact distance calculations, especially in the early and the last stage in its convergence process, and retains the same result as the standard algorithm. This is efficiently performed by two distinct components. One uses the lower bounds on exact distances between objects and centroids for the acceleration in the early stage. The lower bound is calculated by the triangle inequality with newly introduced fixed points called pivots. The other component for the last stage skips the distance calculation between an invariant centroid and an object satisfying the following condition. The cluster to which the object is assigned remains unchanged, i.e., its centroid is also invariant. For much further speed-up, we can incorporate an existing algorithm into the proposed algorithm as an extension, which often performs a complementary role in the middle stage. The experimental results we obtained on large-scale and high-dimensional image data sets demonstrate that given a large k value, the proposed algorithm outperforms existing algorithms in terms of both the reduction rate of distance calculations and elapsed time.

Double-layer neighborhood graph based similarity search for fast query-by-example spoken term detection

This paper presents a novel double-layer neighborhood graph index for acceleration of similarity search that accomplishes fast querybyexample spoken term detection (STD). When a query segment is given, our proposed STD method finds similar segments to the query from an utterance data set by efficient similarity search that traverses the double-layer neighborhood graph (DLG) with a low computational cost. The segment is a sequence of Gaussian mixture model posteriorgram frames and corresponds to a vertex in the DLG. A dissimilarity between vertices is measured by dynamic time warping. The DLG consists of two distinct degree-reduced k-nearest neighbor graphs in a base and an upper layer. The base layers graph has all the vertices in the data set while the upper layers graph includes only representatives extracted from the vertices in the base layer. By way of analogy, search in the DLG resembles driving on general roads and express highways appropriately for travel-time saving. Experimental results on the MIT lecture corpus demonstrate that the proposed method achieves CPU time reduction by 40% and more than 60% compared to the most recent method and the ordinary graphbased method, keeping almost the same precision.