'Pairwise distances' diaporamas de présentation

CSC 2535: 2013 Lecture 11 Non-linear dimensionality reduction

CSC 2535: 2013 Lecture 11 Non-linear dimensionality reduction. Geoffrey Hinton. Dimensionality reduction: Some Assumptions. High-dimensional data often lies on or near a much lower dimensional, curved manifold. A good way to represent data points is by their low-dimensional coordinates.

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Combinatorial Pattern Matching

Combinatorial Pattern Matching. Outline. Microarrays and Introduction to Clustering Hierarchical Clustering K-Means Clustering Corrupted Cliques Problem CAST Clustering Algorithm . Section 1: Microarrays and Introduction to Clustering. What Is Clustering?.

750 views • 59 slides

Processing & Testing Phylogenetic Trees

Processing & Testing Phylogenetic Trees. Rooting. Rooting 1. Outgroup Rooting: Based on external information. 2. Midpoint Rooting: Direct a posteriori use of the ultrametricity assumption.

690 views • 51 slides

FROM PROTEIN SEQUENCES TO PHYLOGENETIC TREES

FROM PROTEIN SEQUENCES TO PHYLOGENETIC TREES. Simon Harris Wellcome Trust Sanger Institute, UK. Agenda. Remind you that molecular phylogenetics is complex the more you know about the compared proteins and the method used, the better Try to avoid the black box approach as much as possible!

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FROM PROTEIN SEQUENCES TO PHYLOGENETIC TREES

FROM PROTEIN SEQUENCES TO PHYLOGENETIC TREES. Simon Harris Wellcome Trust Sanger Institute, UK. Agenda. Remind you that molecular phylogenetics is complex the more you know about the compared proteins and the method used, the better Try to avoid the black box approach as much as possible!

491 views • 47 slides

BCB 444/544

BCB 444/544. Lecture 13 Star Alignment & Clustal (for MSA) Perhaps: Profiles & Hidden Markov Models (HMMs) #13_Sept19. Required Reading ( before lecture). ? Mon Sept 17 - Lecture 12 Position Specific Scoring Matrices & PSI-BLAST Chp 6 - pp 75-78 (but not HMMs)

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NonLinear Dimensionality Reduction or Unfolding Manifolds Tennenbaum|Silva|Langford [Isomap]

NonLinear Dimensionality Reduction or Unfolding Manifolds Tennenbaum|Silva|Langford [Isomap] Roweis|Saul [Locally Linear Embedding] Presented by Vikas C. Raykar | University of Maryland, CollegePark. Dimensionality Reduction.

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NonLinear Dimensionality Reduction or Unfolding Manifolds Tennenbaum|Silva|Langford [Isomap]

NonLinear Dimensionality Reduction or Unfolding Manifolds Tennenbaum|Silva|Langford [Isomap] Roweis|Saul [Locally Linear Embedding] Presented by Vikas C. Raykar | University of Maryland, CollegePark. Dimensionality Reduction.

619 views • 39 slides

Spectral Methods

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Nonlinear Dimensionality Reduction by Locally Linear Embedding

Nonlinear Dimensionality Reduction by Locally Linear Embedding. Sam T. Roweis and Lawrence K. Saul. Presented by Yueng-tien , Lo. Reference: "Nonlinear dimensionality reduction by locally linear embedding," Roweis & Saul, Science, 2000.

461 views • 30 slides

Curvilinear Distance Analysis versus Isomap.

Curvilinear Distance Analysis versus Isomap. Presented By: Avadhanula.Laxminarayana kumar. Venishetty Kalyan Kumar. Motivation. Comparing two nonlinear projection methods.

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Phylogenetic Reconstruction based on RNA Secondary Structural Alignment

Phylogenetic Reconstruction based on RNA Secondary Structural Alignment. Benny Chor, Tel-Aviv Univ. Joint work with Moran Cabili, Assaf Meirovich, and Metsada Pasmanik-Chor. Phylogenetic Trees Based on What ? Morphology (1800 - ) Single gene sequence (DNA or AA) (1960 - ).

366 views • 24 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

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?

321 views • 22 slides

CSC321: Extra Lecture (not on the exam) Non-linear dimensionality reduction

CSC321: Extra Lecture (not on the exam) Non-linear dimensionality reduction. Geoffrey Hinton. Linear methods of reducing dimensionality. PCA finds the directions that have the most variance.

223 views • 22 slides

CSC321: Lecture 25: Non-linear dimensionality reduction

CSC321: Lecture 25: Non-linear dimensionality reduction. Geoffrey Hinton. Linear methods of reducing dimensionality. PCA finds the directions that have the most variance. By representing where each datapoint is along these axes, we minimize the squared reconstruction error.

211 views • 21 slides

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295 views • 17 slides