Topics

We encourage discussions on recent advances, ongoing developments, and novel applications of manifold learning, optimization, feature representations and deep learning techniques. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:

• Theoretical Advances related to manifold learning such as

  • Dimensionality Reduction (e.g., Locally Linear Embedding, Laplacian Eigenmaps and etc.)

  • Clustering (e.g., discriminative clustering)

  • Kernel methods

  • Metric Learning

  • Time series on non-linear manifolds

  • Transfert learning on non-linear manifolds

  • Generative Models on non-linear manifolds

  • Subspace Methods (e.g., Subspace clustering)

  • Advanced Optimization Techniques (constrained and non-convex optimization techniques on non-linear manifolds)

  • Mathematical Models for learning sequences

  • Mathematical Models for learning Shapes

  • Deep learning and non-linear manifolds

  • Low-rank factorization methods

• Applications:

  • Biometrics

  • Image/video recognition

  • Action/activity recognition

  • Facial expressions recognition

  • Learning and scene understanding

  • Medical imaging

  • Robotics

  • Other related topics not listed above