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.
5858271
Prototype vector machine for large scale semi-supervised learning
Practical data mining rarely falls exactly into the supervised learning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computational intensiveness of graph-based SSL arises largely from the manifold or graph regularization, which in turn lead to large models that are difficult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highly scalable, graph-based algorithm for large-scale SSL. Our key innovation is the use of "prototypes vectors" for efficient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of the kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number of machine learning benchmark data sets.
10651609
Improved Nyström low-rank approximation and error analysis
Low-rank matrix approximation is an effective tool in alleviating the memory and computational burdens of kernel methods and sampling, as the mainstream of such algorithms, has drawn considerable attention in both theory and practice. This paper presents detailed studies on the Nyström sampling scheme and in particular, an error analysis that directly relates the Nyström approximation quality with the encoding powers of the landmark points in summarizing the data. The resultant error bound suggests a simple and efficient sampling scheme, the k-means clustering algorithm, for Nyström low-rank approximation. We compare it with state-of-the-art approaches that range from greedy schemes to probabilistic sampling. Our algorithm achieves significant performance gains in a number of supervised/unsupervised learning tasks including kernel PCA and least squares SVM.
2257586
Applying neighborhood consistency for fast clustering and kernel density estimation
Nearest neighborhood consistency is an important concept in statistical pattern recognition, which underlies the well-known k-nearest neighbor method. In this paper, we combine this idea with kernel density estimation based clustering, and derive the fast mean shift algorithm (FMS). FMS greatly reduces the complexity of feature space analysis, resulting satisfactory precision of classification. More importantly, we show that with FMS algorithm, we are in fact relying on a conceptually novel approach of density estimation, the fast kernel density estimation (FKDE) for clustering. The FKDE combines smooth and non-smooth estimators and thus inherits advantages from both. Asymptotic analysis reveals the approximation of the FKDE to standard kernel density estimator. Data clustering and image segmentation experiments demonstrate the efficiency of FMS.
1966273
Block-quantized kernel matrix for fast spectral embedding
Eigendecomposition of kernel matrix is an indispensable procedure in many learning and vision tasks. However, the cubic complexity <i>O</i>(<i>N</i><sup>3</sup>) is impractical for large problem, where <i>N</i> is the data size. In this paper, we propose an efficient approach to solve the eigendecomposition of the kernel matrix <i>W</i>. The idea is to approximate <i>W</i> with <i>W</i> that is composed of <i>m</i><sup>2</sup> constant blocks. The eigenvectors of <i>W</i>, which can be solved in <i>O</i>(<i>m</i><sup>3</sup>) time, is then used to recover the eigenvectors of the original kernel matrix. The complexity of our method is only <i>O</i>(<i>mN</i> + <i>m</i><sup>3</sup>), which scales more favorably than state-of-the-art low rank approximation and sampling based approaches (<i>O</i>(<i>m</i><sup>2</sup><i>N</i> + <i>m</i><sup>3</sup>)), and the approximation quality can be controlled conveniently. Our method demonstrates encouraging scaling behaviors in experiments of image segmentation (by spectral clustering) and kernel principal component analysis.
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.
31414473
Improved Learning Algorithm Based on Generalized SOM for Dynamic Non-linear System
This paper proposes an improved learning algorithm based on generalized SOM for dynamical non-linear system identification. To improve the convergent speed and the accuracy of SOM algorithm, we propose the improved self-organizing algorithm, which, at first, applies the multiple local models instead of the global model, and secondly, adjusts the weights of the computing output layers along with the weights of the competing neurons layer during the training process. We prove that the improved algorithm is convergent if the network has suitable initial weights and small positive real parameters. The simulation results using our improved generalized SOM show an improvement for non-linear system compared to traditional neural network control systems.
32209678
A Novel Coarse-to-Fine Adaptation Segmentation Approach for Cellular Image Analysis
Cellular image content analysis is one of the most important aspects of the cellular research and often requires collecting a great amount of statistical information and phenomena. Automated segmentation of time-lapse images gradually becomes the key problem in cellular image analysis. To address fuzzy, irregular, and ruffling cell boundaries in time-lapse cellular images, this paper introduces a hierarchical coarse-to-fine approach which is composed of iteration-dependent adaptation procedures with high-level interpretation: initial segmentation, adaptive processing, and refined segmentation. The iteration-dependent adaptation lies in that the adaptive processing and the refined segmentation be deliberately designed without a fixed order and a uniform associated iteration number, to connect coarse segmentation and refined segmentation for locally progressive approximation. The initial segmentation could avoid over-segmentation from watershed transform and converge to some features using a priori information. Experimental results on cellular images with spurious branches, arbitrary gaps, low contrast boundaries and low signal-to-noise ratio, show that the proposed approach provides a close matching to the manual cognition and overcomes several common drawbacks from other existing methods applied on cell migration. The procedure configuration of the proposed approach has a certain potential to serve as a biomedical image content analysis tool.
389920
Board Forum Crawling: A Web Crawling Method for Web Forum
We present a new method of board forum crawling to crawl Web forum. This method exploits the organized characteristics of the Web forum sites and simulates human behavior of visiting Web forums. The method starts crawling from the homepage, and then enters each board of the site, and then crawls all the posts of the site directly. Board forum crawling can crawl most meaningful information of a Web forum site efficiently and simply. We experimentally evaluated the effectiveness of the method on real Web forum sites by comparing with the traditional breadth-first crawling. We also used this method in a real project, and 12000 Web forum sites have been crawled successfully. These results show the effectiveness of our method
207113548
A Hybrid Two-Phase Algorithm For Face Recognition
Scientists have developed numerous classifiers in the pattern recognition field, because applying a single classifier is not very conducive to achieve a high recognition rate on face databases. Problems occur when the images of the same person are classified as one class, while they are in fact different in poses, expressions, or lighting conditions. In this paper, we present a hybrid, two-phase face recognition algorithm to achieve high recognition rates on the FERET data set. The first phase is to compress the large class number database size, whereas the second phase is to perform the decision-making. We investigate a variety of combinations of the feature extraction and pattern classification methods. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are examined and tested using 700 facial images of different poses from FERET database. Experimental results show that the two combinations, LDA+LDA and LDA+SVM, outperform the other types of combinations. Meanwhile, when classifiers are considered in the two-phase face recognition, it is better to adopt the L1 distance in the first phase and the class mean in the second phase.
17637228
Research on the Application of Industrial CT for Relics Image Reconstruction
The purpose of this paper is to visualizing the internal structure of historic relics by using the industrial computed-tomography (CT). The image reconstruction of CT scan data can be used to observe and analyze the antique inside that the eye has no way to distinguish without disruption. This paper have built the simulated testing model, produced the data of simulating projection, and accomplished the comparison of image reconstruction between of ART(SIRT) and FDK algorithm. After handling the real data of different materials, the experiment and analysis of CT image reconstruction have been carried out. This job has provided the effective technology support in the research of the heritage computed tomography scan platform. This paper also has provided the information for teaching and learning activities of cultural studies.