8020389
Learning from Labeled and Unlabeled Data Using Random Walks
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to predict the labels of the unlabeled points. Any supervised learning algorithm can be applied to this problem, for instance, Support Vector Machines (SVMs). The problem of our interest is if we can implement a classifier which uses the unlabeled data information in some way and has higher accuracy than the classifiers which use the labeled data only. Recently we proposed a simple algorithm, which can substantially benefit from large amounts of unlabeled data and demonstrates clear superiority to supervised learning methods. Here we further investigate the algorithm using random walks and spectral graph theory, which shed light on the key steps in this algorithm.
7724688
Ranking on Data Manifolds
The Google search engine has enjoyed huge success with its web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the web using random walks. Here we propose a simple universal ranking algorithm for data lying in the Euclidean space, such as text or image data. The core idea of our method is to rank the data with respect to the intrinsic manifold structure collectively revealed by a great amount of data. Encouraging experimental results from synthetic, image, and text data illustrate the validity of our method.
566534
Semi-supervised Protein Classification Using Cluster Kernels
MOTIVATION
Building an accurate protein classification system depends critically upon choosing a good representation of the input sequences of amino acids. Recent work using string kernels for protein data has achieved state-of-the-art classification performance. However, such representations are based only on labeled data--examples with known 3D structures, organized into structural classes--whereas in practice, unlabeled data are far more plentiful.


RESULTS
In this work, we develop simple and scalable cluster kernel techniques for incorporating unlabeled data into the representation of protein sequences. We show that our methods greatly improve the classification performance of string kernels and outperform standard approaches for using unlabeled data, such as adding close homologs of the positive examples to the training data. We achieve equal or superior performance to previously presented cluster kernel methods and at the same time achieving far greater computational efficiency.


AVAILABILITY
Source code is available at www.kyb.tuebingen.mpg.de/bs/people/weston/semiprot. The Spider matlab package is available at www.kyb.tuebingen.mpg.de/bs/people/spider.


SUPPLEMENTARY INFORMATION
www.kyb.tuebingen.mpg.de/bs/people/weston/semiprot.
474823
Boosting the Feature Space: Text Classification for Unstructured Data on the Web
The issue of seeking efficient and effective methods for classifying unstructured text in large document corpora has received much attention in recent years. Traditional document representation like bag-of-words encodes documents as feature vectors, which usually leads to sparse feature spaces with large dimensionality, thus making it hard to achieve high classification accuracies. This paper addresses the problem of classifying unstructured documents on the Web. A classification approach is proposed that utilizes traditional feature reduction techniques along with a collaborative filtering method for augmenting document feature spaces. The method produces feature spaces with an order of magnitude less features compared with a baseline bag-of-words feature selection method. Experiments on both real-world data and benchmark corpus indicate that our approach improves classification accuracy over the traditional methods for both support vector machines and AdaBoost classifiers.
12261862
Capacity of reproducing kernel spaces in learning theory
The capacity of reproducing kernel Hilbert spaces (RKHS) plays an essential role in the analysis of learning theory. Covering numbers and packing numbers of balls of these reproducing kernel spaces are important measurements of this capacity. We first present lower bound estimates for the packing numbers by means of nodal functions. Then we show that if a Mercer kernel is C/sup s/ (for some s>0 being not an even integer), the RKHS associated with this kernel can be embedded into C/sup s/2/. This gives upper-bound estimates for the covering number concerning Sobolev smooth kernels.Examples and applications to V/sub /spl gamma// dimension and Tikhonov (1977) regularization are presented to illustrate the upper- and lower-bound estimates.
8249361
SUDEPHIC: Self-Tuning Density-Based Partitioning and Hierarchical Clustering
Clustering is one of the primary techniques in data mining, for which to find the user expecting result is a major issue. However, to dynamically specify the parameters for clustering algorithms presents an obstacle for users. This paper firstly introduces a novel density-based partitioning and hierarchical algorithm, which makes it easy to employ synthetic feedback mechanism in clustering. Additionally, by investigating into the relation between parameters and the clustering result, we propose a self-tuning technique for the setting of parameters. Meanwhile, the density distribution within a cluster can be expressed in the result for the user to specify the cluster’s feature. The algorithm is both evaluated in theory and practice. It outperforms many existing algorithms both in efficiency and quality.
13242297
A clustering method for web data with multi-type interrelated components
Traditional clustering algorithms work on "flat" data, making the assumption that the data instances can only be represented by a set of homogeneous and uniform features. Many real world data, however, is heterogeneous in nature, comprising of multiple types of interrelated components. We present a clustering algorithm, K-SVMeans, that integrates the well known K-Means clustering with the highly popular Support Vector Machines(SVM) in order to utilize the richness of data. Our experimental results on authorship analysis of scientific publications show that K-SVMeans achieves better clustering performance than homogeneous data clustering.
206963206
Efficient adaptive Volterra filters for active nonlinear noise control with a linear secondary-path
Using a nonlinear filter to control a linear device has been studied in Strauch and Mulgrew (1998), and been proved to be effective in active noise control (ANC) when: i) the secondary path has a linear and nonminimum phase transfer function, and the reference noise is a nonlinear, predictable noise or non-Gaussian noise; ii) the primary path has a nonlinear effect. Tan et al. (2001) reinforced these statements and successfully implemented this idea with an adaptive Volterra filter, and developed an algorithm called the Volterra filtered-x LMS (VFXLMS) algorithm. However this VFXLMS suffers from a heavy computational burden as well as stability problems. In this paper, we provide several alternatives to VFXLMS: the modified Volterra LMS algorithm that increases the convergence rate by slightly increasing the computational burden, the filtered-error Volterra LMS algorithms (including the adjoint Volterra LMS algorithm and the secondary-path equalization Volterra LMS algorithm) that greatly reduce the computational complexity and improves the convergence rate. Analysis and simulation results prove the effectiveness of our proposed adaptive algorithms.