Kooperativer Bibliotheksverbund

Berlin Brandenburg

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Language: English
    In: IEEE Transactions on Knowledge and Data Engineering, 01 November 2016, Vol.28(11), pp.2884-2894
    Description: In this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is a hierarchical algorithm, which iteratively minimizes the quantization error by jointly optimizing the codebooks in each layer, using a gradient decent approach. An extensive set of experimental results and comparative evaluations show that CompQ outperforms the-state-of-the-art while retaining a comparable computational complexity.
    Keywords: Vector Quantization ; Encoding ; Optimization ; Hamming Distance ; Electronic Mail ; Nearest Neighbor Searches ; Approximate Nearest Neighbor Search ; Binary Codes ; Large-Scale Retrieval ; Vector Quantization ; Engineering ; Computer Science
    ISSN: 1041-4347
    E-ISSN: 1558-2191
    Source: IEEE Conference Publications
    Source: IEEE Journals & Magazines 
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages