5878411
Density-Weighted Nystrm Method for Computing Large Kernel Eigensystems
The Nystrm method is a well-known sampling-based technique for approximating the eigensystem of large kernel matrices. However, the chosen samples in the Nystrm method are all assumed to be of equal importance, which deviates from the integral equation that defines the kernel eigenfunctions. Motivated by this observation, we extend the Nystrm method to a more general, density-weighted version. We show that by introducing the probability density function as a natural weighting scheme, the approximation of the eigensystem can be greatly improved. An efficient algorithm is proposed to enforce such weighting in practice, which has the same complexity as the original Nystrm method and hence is notably cheaper than several other alternatives. Experiments on kernel principal component analysis, spectral clustering, and image segmentation demonstrate the encouraging performance of our algorithm.
7532408
Simplifying Mixture Models Through Function Approximation
The finite mixture model is widely used in various statistical learning problems. However, the model obtained may contain a large number of components, making it inefficient in practical applications. In this paper, we propose to simplify the mixture model by minimizing an upper bound of the approximation error between the original and the simplified model, under the use of the L 2 distance measure. This is achieved by first grouping similar components together and then performing local fitting through function approximation. The simplified model obtained can then be used as a replacement of the original model to speed up various algorithms involving mixture models during training (e.g., Bayesian filtering, belief propagation) and testing [e.g., kernel density estimation, support vector machine (SVM) testing]. Encouraging results are observed in the experiments on density estimation, clustering-based image segmentation, and simplification of SVM decision functions.
973036
Accelerated Convergence Using Dynamic Mean Shift
Mean shift is an iterative mode-seeking algorithm widely used in pattern recognition and computer vision. However, its convergence is sometimes too slow to be practical. In this paper, we improve the convergence speed of mean shift by dynamically updating the sample set during the iterations, and the resultant procedure is called dynamic mean shift (DMS). When the data is locally Gaussian, it can be shown that both the standard and dynamic mean shift algorithms converge to the same optimal solution. However, while standard mean shift only has linear convergence, the dynamic mean shift algorithm has superlinear convergence. Experiments on color image segmentation show that dynamic mean shift produces comparable results as the standard mean shift algorithm, but can significantly reduce the number of iterations for convergence and takes much less time.
6920854
Chaos inducement and Enhancement in Two Particular Nonlinear Maps Using Weak Periodic/quasiperiodic Perturbations
Weak periodic perturbation has long been used to suppress chaos in dynamical systems. In this paper, however, we demonstrate that weak periodic or quasiperiodic perturbation can also be used to induce chaos in nonchaotic parameter ranges of chaotic maps, or to enhance the already existing chaotic state. Two kinds of chaotic maps, the period doubling system and the Hopf bifurcation system, are employed as basic models to analyze and compare in detail the different mechanisms of inducing and enhancing chaos in them. In addition, a special kind of intermittency characterized by its periodicity is found for the first time in periodically perturbed Henon map, and reasonable speculations are presented to explain its complicated dynamics.
2688754
A method for evaluating upper bound of simultaneous switching gates using circuit partition
This paper presents a method for evaluating an upper bound of simultaneous switching gates in combinational circuits. In this method, the original circuit is partitioned into subcircuits, and the upper bound is approximately computed as the sum of maximum switching gates for all subcircuits. In order to increase the accuracy, we adopted an evaluation function that takes account of both the interconnections among subcircuits and the number of generated subcircuits. Experimental results for ISCAS circuits show that the method efficiently evaluates the upper bounds of switching gates.
13932512
Joint Transmit Rate, Power and Antenna Allocation for MIMO Systems with Multimedia Traffic
This paper proposes an adaptive transmission scheme for MIMO systems to provide different bit error rates and transmission rates for multimedia traffic. The adaptive transmission scheme allocates antennas, rate and power jointly according to the feedback information to satisfy the diverse QoS requirements of the multimedia traffic. Furthermore, an efficient search algorithm with low complexity is proposed for practical implementation. Simulation results show that the proposed scheme improves the spectral efficiency while guaranteeing the QoS requirements of multimedia traffic. Moreover, the proposed search algorithm achieves close optimal performance with great complexity reduction.
17724018
Logical structure based semantic relationship extraction from semi-structured documents
Addressed in this paper is the issue of semantic relationship extraction from semi-structured documents. Many research efforts have been made so far on the semantic information extraction. However, much of the previous work focuses on detecting `isolated' semantic information by making use of linguistic analysis or linkage information in web pages and limited research has been done on extracting semantic relationship from the semi-structured documents. In this paper, we propose a method for semantic relationship extraction by using the logical information in the semi-structured document (semi-structured document usually has various types of structure information, e.g. a semi-structured document may be hierarchical laid out). To the best of our knowledge, extracting semantic relationships by using logical information has not been investigated previously. A probabilistic approach has been proposed in the paper. Features used in the probabilistic model have been defined.
14385690
A New Least Squares Subspace Projection Approach to Unmix Hyperspectral Data
Linearly constrained discriminant analysis (LCDA) and orthogonal subspace projection (OSP) are both explored in hyperspectral image classification and have shown promise in signature detection, discrimination and classification. However, the two subspace projection approaches cannot directly estimate the signature abundance. The OSP has been extended by a least squares orthogonal subspace projection (LSOSP) to estimate the signature abundance while LCDA has not. The solution of LCDA turns out to be a constrained version of OSP implemented with a data whitening process and the means of samples as signatures. Due to this fact, following the same idea for extending OSP to LSOSP, in this paper, a modified linearly constrained discriminant analysis (MLCDA) is proposed for unmixing hyperspectral data, which can directly estimate the signature abundance. Experiment results obtained from both artificial simulated and practical remote sensing data demonstrate that the MLCDA algorithm performs better than least squares method and the LSOSP.
15937167
High-throughput layered decoder implementation for quasi-cyclic LDPC codes
This paper presents a high-throughput decoder design for the Quasi-Cyclic (QC) Low-Density Parity-Check (LDPC) codes. Two new techniques are proposed, including parallel layered decoding architecture (PLDA) and critical path splitting. PLDA enables parallel processing for all layers by establishing dedicated message passing paths among them. The decoder avoids crossbar-based large interconnect network. Critical path splitting technique is based on articulate adjustment of the starting point of each layer to maximize the time intervals between adjacent layers, such that the critical path delay can be split into pipeline stages. Furthermore, min-sum and loosely coupled algorithms are employed for area efficiency. As a case study, a rate-1/2 2304-bit irregular LDPC decoder is implemented using ASIC design in 90 nm CMOS process. The decoder can achieve the maximum decoding throughput of 2.2 Gbps at 10 iterations. The operating frequency is 950 MHz after synthesis and the chip area is 2.9 mm2.