Fast ISOMAP Based on Minimum Set Coverage.
ABSTRACT Isometric feature mapping (ISOMAP) has two computational bottlenecks. The first is calculating the N×N graph distance matrix
. Using Floyd’s algorithm, this is O(N
3); this can be improved to O(kN
2 log N) by implementing Dijkstra’s algorithm. The second bottleneck is the MDS eigenvalue calculation, which involves a full N×N
matrix and has complexity O(N
3). In this paper, we address both of these inefficiencies by a greedy approximation algorithm of minimum set coverage (MSC).
The algorithm learns a minimum subset of overlapping neighborhoods for high dimensional data that lies on or near a low dimensional
manifold. The new framework leads to order-of-magnitude reductions in computation time and makes it possible to study much
larger problems in manifold learning.
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ABSTRACT: An unsupervised learning algorithm is presented for segmentation and evaluation of motion data from the on-body Orient wireless motion capture system for mobile gait analysis. The algorithm is model-free and operates on the latent space of the motion, by first aggregating all the sensor data into a single vector, and then modeling them on a low-dimensional manifold to perform segmentation. The proposed approach is contrasted to a basic, model-based algorithm, which operates directly on the joint angles computed by the Orient sensor devices. The latent space algorithm is shown to be capable of retrieving qualitative features of the motion even in the face of noisy or incomplete sensor readings.ACM Transactions on Embedded Computing Systems (TECS). 06/2013; 12(4).