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Power weighted shortest paths for clustering Euclidean data

The first author gratefully acknowledges the support of the Department of Mathematics, The University of Georgia, where the first author was a graduate student while this work was completed. The second author thanks the Department of Mathematics, The University of Michigan for their support. Both authors thank the anonymous reviewer for many useful suggestions.

• We study the use of power weighted shortest path metrics for clustering high dimensional Euclidean data, under the assumption that the data is drawn from a collection of disjoint low dimensional manifolds. We argue, theoretically and experimentally, that this leads to higher clustering accuracy. We also present a fast algorithm for computing these distances.

Mathematics Subject Classification: Primary: 62H30, 58F15; Secondary: 05C85.

 Citation:

• Figure 1.  Three sample geodesics in the power weighted shortest path metric with $p=2$, for the data set "Three Lines" (see §6). Observe how the geodesics consist of many small hops, instead of several large hops. The total lengths of the red and green paths are significantly smaller than the length of the blue path

Figure 2.  All three synthetic data sets, projected into $\mathbb{R}^{2}$. From left to right: Three Lines, Three Moons and Three Circles

Figure 3.  Varying $p$ and recording the accuracy of spectral clustering on the Three Lines data set, for three different values of the ambient dimension

Table 1.  Classification accuracy of spectral clustering. Note that $A^{(1)}$ represents using the Euclidean metric

 $A^{(f, 1)}$ $A^{(1)}$ $A^{(2)}$ $A^{(10)}$ $A^{(\infty)}$ 3 Lines $66.11\pm 0.94\%$ $66.35 \pm 3.73\%$ $66.87 \pm 3.37\%$ $95.38\pm 9.22\%$ $\bf{95.38 \pm 9.1\%}$ 3 Moons $85.90 \pm 1.13\%$ $94.40 \pm 1.48\%$ $94.40 \pm 1.48\%$ $\bf{96.20 \pm 1.76\%}$ $94.35 \pm 3.34\%$ 3 Circles $51.87 \pm 0.00\%$ $51.93 \pm 0.32\%$ $51.94 \pm 0.36\%$ $71.22 \pm 9.50\%$ $\bf{73.61 \pm 10.47\%}$ $\mathtt{ DrivFace}$ $78.88\%$ $71.62\%$ $71.62\%$ $74.71\%$ $\bf{85.38\%}$ $\mathtt{ COIL-20}$ $63.24\%$ $75.28\%$ $\bf{78.61\%}$ $77.45\%$ $60.92\%$ $\mathtt{ OptDigits}$ $77.73\%$ $91.49\%$ $\bf{91.54\%}$ $88.39\%$ $83.17\%$ $\mathtt{ USPS}$ $48.65\%$ $65.05\%$ $65.02\%$ $76.20\%$ $\bf{77.92\%}$ $\mathtt{ MNIST}$ - $76.11\%$ $75.63\%$ $84.54\%$ $\bf{86.77\%}$

Table 2.  Run time of spectral clustering, in seconds. Note that this includes the time rquired to construct the similarity matrix. $A^{(1)}$ represents using the Euclidean metric

 $A^{(f, 1)}$ $A^{(1)}$ $A^{(2)}$ $A^{(10)}$ $A^{(\infty)}$ 3 Lines $0.32$ $0.16$ $1.20$ $1.22$ $1.22$ 3 Moons $0.33$ $0.17$ $1.31$ $1.30$ $1.36$ 3 Circles $0.35$ $0.16$ $1.00$ $1.06$ $1.07$ $\mathtt{ DrivFace}$ $0.37$ $1.24$ $1.55$ $1.64$ $1.64$ $\mathtt{ COIL-20}$ $0.57$ $0.72$ $1.57$ $1.82$ $1.78$ $\mathtt{ OptDigits}$ $5.40$ $1.41$ $5.28$ $5.58$ $5.67$ $\mathtt{ USPS}$ $27.40$ $17.12$ $26.75$ $22.78$ $23.79$ $\mathtt{ MNIST}$ - $2060.23$ $2031.38$ $1554.15$ $1613.41$
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