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Conference Papers Year : 2012

Neighbor Embedding with Non-negative Matrix Factorization for image prediction

Abstract

The paper studies several non-negative matrix factorization methods with nearest neighbors constrained dictionaries for image prediction. The methods considered include the multiplicative update algorithm, the projected gradient algorithm, as well as the graph-regularized NMF solution which aims at taking into account the geometrical structure of the input data. The Intra prediction problem based on these NMF solutions amounts to a neighbor embedding problem. Both prediction and rate-distortion performances are then given in comparison with other neighbor embedding methods like locally linear embedding (LLE) and locally linear embedding with low dimensional neigborhood representation (LLE-LDNR).
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Dates and versions

hal-00755720 , version 1 (21-11-2012)

Identifiers

  • HAL Id : hal-00755720 , version 1

Cite

Christine Guillemot, Mehmet Turkan. Neighbor Embedding with Non-negative Matrix Factorization for image prediction. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, Mar 2012, Kyoto, Japan. pp.785-788. ⟨hal-00755720⟩
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