572 research outputs found
Fast Approximate -Means via Cluster Closures
-means, a simple and effective clustering algorithm, is one of the most
widely used algorithms in multimedia and computer vision community. Traditional
-means is an iterative algorithm---in each iteration new cluster centers are
computed and each data point is re-assigned to its nearest center. The cluster
re-assignment step becomes prohibitively expensive when the number of data
points and cluster centers are large.
In this paper, we propose a novel approximate -means algorithm to greatly
reduce the computational complexity in the assignment step. Our approach is
motivated by the observation that most active points changing their cluster
assignments at each iteration are located on or near cluster boundaries. The
idea is to efficiently identify those active points by pre-assembling the data
into groups of neighboring points using multiple random spatial partition
trees, and to use the neighborhood information to construct a closure for each
cluster, in such a way only a small number of cluster candidates need to be
considered when assigning a data point to its nearest cluster. Using complexity
analysis, image data clustering, and applications to image retrieval, we show
that our approach out-performs state-of-the-art approximate -means
algorithms in terms of clustering quality and efficiency
Research into the characteristics of horizontal gaseous jets underwater
The gaseous jet of the solid rocket motor running under water is influenced by both gravity and buoyancy, which will have a significant influence on the flow field structure and thrust of the motor, especially in the initial period when vehicles are launching horizontally. The work in this paper consists of three parts: firstly, to understand the mechanism of the thrust oscillation characteristics and the jet structure of a solid rocket motor running under water, as affected by gravity and buoyancy, a 3-d numerical simulation using the volume of fluid (VOF) model was established. Compared with those results neglecting gravity and buoyancy, conclusions were obtained that the jet structure, considering gravity and buoyancy, are more consistent with the experimental results. Secondly, the principle of momentum was used to analyze the flow field structure and the thrust oscillating characteristics in the initial period of operation. Finally, after analyzing the density gradient along the axis of the motor, results indicated that the length of jets under the influence of gravity and buoyancy varies linearly and the slope of the line is related to the working conditions. Comparing the trajectories under different conditions, a common theme emerged. The laws of thermodynamics are also used to simulate the jets as gas-water-vapor, three-phase, systems. The effect of phase transitions on the structure of jets and the characteristics of their thrust form the key conclusions
Optimized Cartesian -Means
Product quantization-based approaches are effective to encode
high-dimensional data points for approximate nearest neighbor search. The space
is decomposed into a Cartesian product of low-dimensional subspaces, each of
which generates a sub codebook. Data points are encoded as compact binary codes
using these sub codebooks, and the distance between two data points can be
approximated efficiently from their codes by the precomputed lookup tables.
Traditionally, to encode a subvector of a data point in a subspace, only one
sub codeword in the corresponding sub codebook is selected, which may impose
strict restrictions on the search accuracy. In this paper, we propose a novel
approach, named Optimized Cartesian -Means (OCKM), to better encode the data
points for more accurate approximate nearest neighbor search. In OCKM, multiple
sub codewords are used to encode the subvector of a data point in a subspace.
Each sub codeword stems from different sub codebooks in each subspace, which
are optimally generated with regards to the minimization of the distortion
errors. The high-dimensional data point is then encoded as the concatenation of
the indices of multiple sub codewords from all the subspaces. This can provide
more flexibility and lower distortion errors than traditional methods.
Experimental results on the standard real-life datasets demonstrate the
superiority over state-of-the-art approaches for approximate nearest neighbor
search.Comment: to appear in IEEE TKDE, accepted in Apr. 201
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