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A Strategy for Manifold Clustering with Sample Algorithms. Sheng Yu UM Statistics. Outline. Motivation Strategy Sample Algorithms. Motivation (pattern). Most current clustering methods are only able to detect agglomerated patterns.
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A Strategy for Manifold Clustering with Sample Algorithms Sheng Yu UM Statistics
Outline • Motivation • Strategy • Sample Algorithms
Motivation (pattern) • Most current clustering methods are only able to detect agglomerated patterns. • New generation methods, such as normalized cut, have more flexibility, but are still not able to detect twisted, perhaps also entangled manifolds. • Such manifold patterns are not rare.
Motivation (noise) • Theoretically, hierarchical clustering method using “single linkage” as the merging criterion is able to cluster twisted patterns. However, since “single linkage” is extremely sensitive to noisy, it is not actually a usable method.
Motivation • To design a new method that is not only able to accomplish traditional “easy” tasks, but also handles twisted, entangled patterns as well. • Also, this new method should not be ruined by noise (moderate level, in terms of signal-noise ratio).
Outline • Motivation • Strategy • Sample Algorithms
Strategy (design) • Engine: Searches paths between each pair of points. More powerful engine provides faster speed. • Filter: Tells the engine which neighboring points can be connected from a specific start point. Controls the quality.
Outline • Motivation • Strategy • Sample Algorithms
Algorithms (filter) • The filter I currently use is still primitive. • But it does a lot of jobs, such as the above examples. • The strategy is an open framework. We can build better filters to detect even more difficult patterns and have more resistance to noise.
Algorithms • The true benefit of a super fast engine is that it allows us to do iteration. • We need to set up a range of acceptable number of clusters. • We do not need our initial parameters to be precise. The algorithm will do heuristic search for us.