'Manifold learning' presentation slideshows

Adaptive Graph Construction and Dimensionality Reduction

Adaptive Graph Construction and Dimensionality Reduction. Songcan Chen, Lishan Qiao, Limei Zhang http://parnec.nuaa.edu.cn/ {s.chen, qiaolishan, zhanglimei}@nuaa.edu.cn. 2009. 11. 06. Outline. Why to construct graph? Typical graph construction Review & Challenges Our works

1.03k views • 62 slides

Dependency Modeling for Information Fusion with Applications in Visual Recognition

Dependency Modeling for Information Fusion with Applications in Visual Recognition. Andy Jinhua MA Advisor: Prof. Pong Chi YUEN. Outline. Motivation Related Works Supervised Spatio -Temporal Manifold Learning Linear Dependency Modeling Reduced Analytic Dependency Modeling

687 views • 57 slides

Dependency Modeling for Information Fusion with Applications in Visual Recognition

Dependency Modeling for Information Fusion with Applications in Visual Recognition. Andy Jinhua MA Instructor: Prof. Pong Chi YUEN. Outline. Motivation Related Works Supervised Spatio -Temporal Manifold Learning Linear Dependency Modeling Reduced Analytic Dependency Modeling

673 views • 57 slides

Richard Baraniuk Mark Davenport Marco Duarte Chinmay Hegde Rice University Michael Wakin

Manifold Models for Signal Acquisition, Compression and Processing. Richard Baraniuk Mark Davenport Marco Duarte Chinmay Hegde Rice University Michael Wakin Colorado School of Mines. Supported by NSF, ONR, ARO, AFOSR, DARPA, Texas Instruments. Digital Sensing Revolution.

685 views • 54 slides

Topology in Manifold Learning

Topology in Manifold Learning. Jonathan Huang Presented at misc-read, 11.22.06. Bibliography. *Simultaneous Inference of View and Body Pose Using Torus Manifolds* Chan-Su Lee and Ahmed Elgammal The 18 th International Conference on Pattern Recognition (ICPR), Hong Kong, August 21-24, 2006

1.24k views • 37 slides

Manifold Learning Via Homology

Manifold Learning Via Homology. Presenter: Ronen Talmon Topological Methods in Electrical Engineering and Networks January 19, 2011. Sources.

564 views • 36 slides

Richard G. Baraniuk Chinmay Hegde

Manifold Learning in the Wild A New Manifold Modeling and Learning Framework for Image Ensembles Aswin C. Sankaranarayanan Rice University. Richard G. Baraniuk Chinmay Hegde. Sensor Data Deluge. Internet Scale Databases. Tremendous size of corpus of available data

608 views • 36 slides

Nonlinear Dimensionality Reduction for Hyperspectral Image Classification

Nonlinear Dimensionality Reduction for Hyperspectral Image Classification . Tim Doster Advisors: John Benedetto & Wojciech Czaja. Outline. Introduction Dimensionality Reduction Algorithms Implementation Challenges Validation Next Semester. High Dimensional Data.

713 views • 35 slides

Applications

412 views • 34 slides

Manifold Learning

324 views • 30 slides

Manifold learning

Manifold learning. Xin Yang. Outline. Manifold and Manifold Learning Classical Dimensionality Reduction Semi-Supervised Nonlinear Dimensionality Reduction Experiment Results Conclusions. What is a manifold?. Examples: sphere and torus. Why we need manifold?. Manifold learning.

473 views • 29 slides

Humans Learn Using Manifolds, Reluctantly

Humans Learn Using Manifolds, Reluctantly. Bryan Gibson, Xiaojin Zhu, Timothy Rogers, Charles Kalish, and Joseph Harrison University of Wisconsin-Madison. A Familiar Task. Question : which one would humans do? Contribution : an empirical study of human manifold learning behaviors.

366 views • 26 slides

Manifold Alignment for Multitemporal Hyperspectral Image Classification

Manifold Alignment for Multitemporal Hyperspectral Image Classification. H. Lexie Yang 1 , Melba M. Crawford 2 School of Civil Engineering, Purdue University and Laboratory for Applications of Remote Sensing Email: {hhyang 1 , mcrawford 2 }@ purdue.edu July 29, 2011

384 views • 23 slides

Manifold learning: Locally Linear Embedding

Manifold learning: Locally Linear Embedding. Jieping Ye Department of Computer Science and Engineering Arizona State University http://www.public.asu.edu/~jye02. Review: MDS. Metric scaling (classical MDS) assuming D is the squared distance matrix. Non-metric scaling

772 views • 23 slides

Nonlinear Dimensionality Reduction Frameworks

Nonlinear Dimensionality Reduction Frameworks. Rong Xu Chan su Lee. Outline. Intuition of Nonlinear Dimensionality Reduction(NLDR) ISOMAP LLE NLDR in Gait Analysis. Intuition: how does your brain store these pictures?. Brain Representation. Brain Representation. Every pixel?

532 views • 22 slides

Non-Isometric Manifold Learning Analysis and an Algorithm

396 views • 22 slides

Di LIU, Meilan WANG, Xu HAN

Di LIU, Meilan WANG, Xu HAN. Manifold Learning Methods Comparison for Ordering Heads Direction - CSIC5011 2019 Spring Project2. Outline. Introduction Dataset Methods Performance Evaluation Conclusion and Further work Reference. Introduction. Face P ose D etermination

145 views • 14 slides

Manifold Learning: ISOMAP Alan O'Connor April 29, 2008

228 views • 11 slides

Seminar on Intelligent Information

132 views • 5 slides