15,051 research outputs found
Online Sequential Projection Vector Machine with Adaptive Data Mean Update
We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM
CVABS: Moving Object Segmentation with Common Vector Approach for Videos
Background modelling is a fundamental step for several real-time computer
vision applications that requires security systems and monitoring. An accurate
background model helps detecting activity of moving objects in the video. In
this work, we have developed a new subspace based background modelling
algorithm using the concept of Common Vector Approach with Gram-Schmidt
orthogonalization. Once the background model that involves the common
characteristic of different views corresponding to the same scene is acquired,
a smart foreground detection and background updating procedure is applied based
on dynamic control parameters. A variety of experiments is conducted on
different problem types related to dynamic backgrounds. Several types of
metrics are utilized as objective measures and the obtained visual results are
judged subjectively. It was observed that the proposed method stands
successfully for all problem types reported on CDNet2014 dataset by updating
the background frames with a self-learning feedback mechanism.Comment: 12 Pages, 4 Figures, 1 Tabl
Sequential Adaptive Detection for In-Situ Transmission Electron Microscopy (TEM)
We develop new efficient online algorithms for detecting transient sparse
signals in TEM video sequences, by adopting the recently developed framework
for sequential detection jointly with online convex optimization [1]. We cast
the problem as detecting an unknown sparse mean shift of Gaussian observations,
and develop adaptive CUSUM and adaptive SSRS procedures, which are based on
likelihood ratio statistics with post-change mean vector being online maximum
likelihood estimators with . We demonstrate the meritorious performance
of our algorithms for TEM imaging using real data
An Online Parallel and Distributed Algorithm for Recursive Estimation of Sparse Signals
In this paper, we consider a recursive estimation problem for linear
regression where the signal to be estimated admits a sparse representation and
measurement samples are only sequentially available. We propose a convergent
parallel estimation scheme that consists in solving a sequence of
-regularized least-square problems approximately. The proposed scheme
is novel in three aspects: i) all elements of the unknown vector variable are
updated in parallel at each time instance, and convergence speed is much faster
than state-of-the-art schemes which update the elements sequentially; ii) both
the update direction and stepsize of each element have simple closed-form
expressions, so the algorithm is suitable for online (real-time)
implementation; and iii) the stepsize is designed to accelerate the convergence
but it does not suffer from the common trouble of parameter tuning in
literature. Both centralized and distributed implementation schemes are
discussed. The attractive features of the proposed algorithm are also
numerically consolidated.Comment: Part of this work has been presented at The Asilomar Conference on
Signals, Systems, and Computers, Nov. 201
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