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Sparse Manifold Learning and Its Applications in Image Classification

Published: 10 July 2014 Publication History

Abstract

Graph-based dimensionality reduction algorithms are important and have been commonly applied in image classification and computer vision applications. To date many approaches have been proposed, e.g. Laplacian Eigenmaps (LE), Locally Linear Embedding (LLE), Locality Preserving Projections (LPP) and ISOMAP and so on. However, all these methods need to set the k nearest neighbor parameter to address the problem. In this paper, we proposed Sparse Patch Alignment Framework to settle it. Patch Alignment Framework which unified manifold learning algorithms through two stages: local patch optimization and whole alignment. We use Sparse Coding to construct the local patch instead of using KNN, thus, the k nearest neighbor parameter is set adaptively. A lot of experiments are done to show the performance of our method. The experiment results illustrate that our method is stable and robust.

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cover image ACM Other conferences
ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service
July 2014
430 pages
ISBN:9781450328104
DOI:10.1145/2632856
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • NSF of China: National Natural Science Foundation of China
  • Beijing ACM SIGMM Chapter

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2014

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Author Tags

  1. Dimension reduction
  2. Sparse Coding
  3. image classification
  4. patch alignment framework

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ICIMCS '14

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Overall Acceptance Rate 163 of 456 submissions, 36%

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