Testing the Manifold Hypothesis Hari Narayanan University of Washington In collaboration with...

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Testing the Manifold Hypothesis Hari Narayanan University of Washington In collaboration with Charles Fefferman and Sanjoy Mitter Princeton MIT

Transcript of Testing the Manifold Hypothesis Hari Narayanan University of Washington In collaboration with...

Page 1: Testing the Manifold Hypothesis Hari Narayanan University of Washington In collaboration with Charles Fefferman and Sanjoy Mitter Princeton MIT.

Testing the Manifold Hypothesis

Hari NarayananUniversity of Washington

In collaboration with Charles Fefferman and Sanjoy Mitter

Princeton MIT

Page 2: Testing the Manifold Hypothesis Hari Narayanan University of Washington In collaboration with Charles Fefferman and Sanjoy Mitter Princeton MIT.

Manifold learning and manifold hypothesis

[Kambhatla-Leen’93, Tannenbaum et al’00, Roweis-Saul’00, Belkin-Niyogi’03, Donoho-Grimes’04]

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When is the Manifold Hypothesis true?

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Reach of a submanifold of Rn

Large reach Small reach

M reach

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Low dimensional manifolds with bounded volume and reach

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Testing the Manifold Hypothesis

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Sample Complexity of testing the manifold hypothesis

[

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Algorithmic question

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Sample complexity of testing the Manifold Hypothesis

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Empirical Risk Minimization

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Fitting manifolds

TexPoint Display

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Reduction to k-means

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Proving a Uniform bound for k-means

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Fat-shattering dimension

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Bound on sample complexity

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VC dimension

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VC dimension

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Random projection

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Random projection

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Bound on sample complexity

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Fitting manifolds

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Algorithmic question

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Outline

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Outline

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Outline

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(3) Generating a smooth vector bundle

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(3) Generating a smooth vector bundle

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Outline

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(4) Generating a putative manifold

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(4) Generating a putative manifold

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(4) Generating a putative manifold

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(4) Generating a putative manifold

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Outline

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(5) Bundle map

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(5) Bundle map

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Outline

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Outline

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Concluding Remarks

•An algorithm for testing the manifold hypothesis.

Future directions:(a)Make practical and test on real data(b)Improve precision in the reach – get rid of controlled constants depending on d.(c)Better algorithms under distributional assumptions

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Thank You!