document: Ensemble-index : a new approach to indexing large databases The problem of similarity search ( query-by-content ) has attracted much research interest . It is a difficult problem because of the inherently high dimensionality of the data . The most promising solutions involve performing dimensionality reduction on the data , then indexing the reduced data with a multidimensional index structure . Many dimensionality reduction techniques have been proposed , including Singular Value Decomposition ( SVD ) , the Discrete Fourier Transform ( DFT ) , the Discrete Wavelet Transform ( DWT ) and Piecewise Polynomial Approximation . In this work , we introduce a novel framework for using ensembles of two or more representations for more efficient indexing . The basic idea is that instead of committing to a single representation for an entire dataset , different representations are chosen for indexing different parts of the database . The representations are chosen based upon a local view of the database . For example , sections of the data that can achieve a high fidelity representation with wavelets are indexed as wavelets , but highly spectral sections of the data are indexed using the Fourier transform . At query time , it is necessary to search several small heterogeneous indices , rather than one large homogeneous index . As we will theoretically and empirically demonstrate this results in much faster query response times .
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