5644766
Transfer learning for collaborative filtering via a rating-matrix generative model
Cross-domain collaborative filtering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative filtering. We first show that the relatedness across multiple rating matrices can be established by finding a shared implicit cluster-level rating matrix, which is next extended to a cluster-level rating model. Consequently, a rating matrix of any related task can be viewed as drawing a set of users and items from a user-item joint mixture model as well as drawing the corresponding ratings from the cluster-level rating model. The combination of these two models gives the RMGM, which can be used to fill the missing ratings for both existing and new users. A major advantage of RMGM is that it can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap. We evaluate the RMGM empirically on three real-world collaborative filtering data sets to show that RMGM can outperform the individual models trained separately.
672476
Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction
The sparsity problem in collaborative filtering (CF) is a major bottleneck for most CF methods. In this paper, we consider a novel approach for alleviating the sparsity problem in CF by transferring useritem rating patterns from a dense auxiliary rating matrix in other domains (e.g., a popular movie rating website) to a sparse rating matrix in a target domain (e.g., a new book rating website). We do not require that the users and items in the two domains be identical or even overlap. Based on the limited ratings in the target matrix, we establish a bridge between the two rating matrices at a cluster-level of user-item rating patterns in order to transfer more useful knowledge from the auxiliary task domain. We first compress the ratings in the auxiliary rating matrix into an informative and yet compact cluster-level rating pattern representation referred to as a codebook. Then, we propose an efficient algorithm for reconstructing the target rating matrix by expanding the codebook. We perform extensive empirical tests to show that our method is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary tasks, as compared to many state-of-the-art CF methods.
13758087
Support cluster machine
For large-scale classification problems, the training samples can be clustered beforehand as a downsampling pre-process, and then only the obtained clusters are used for training. Motivated by such assumption, we proposed a classification algorithm, Support Cluster Machine (SCM), within the learning framework introduced by Vapnik. For the SCM, a compatible kernel is adopted such that a similarity measure can be handled not only between clusters in the training phase but also between a cluster and a vector in the testing phase. We also proved that the SCM is a general extension of the SVM with the RBF kernel. The experimental results confirm that the SCM is very effective for largescale classification problems due to significantly reduced computational costs for both training and testing and comparable classification accuracies. As a by-product, it provides a promising approach to dealing with privacy-preserving data mining problems.
17395966
Scene segmentation based on video structure and spectral methods
Scene is an important semantic unit for video analysis, retrieval and browsing. However, due to the lack of a generic algorithm, many studies focus on specific methods for certain video genes, e.g., news, sports, etc. In this paper, we propose a general framework for scene segmentation. First, we construct a graph, in which the elements encode the shot-to-shot coherent characteristics of a video clip based on visual similarity and temporal relation between shots. In this step, we only exploit the inherent property of video itself and it is independent of video genres. Second, spectral method is applied on the graph to group shots into scenes. The proposed method is not only simple but also effective to deal with organized features. Experimental results validate the robustness of our method on different kinds of videos.
30055456
A new block detection receiver for noncoherent orthogonal CDMA system
A new block detection receiver is presented for noncoherent orthogonal CDMA (such as the CDMA IS-95A reverse link). This new receiver uses multiple Hadamard (or Walsh) codes for joint detection instead of using a single Walsh code as conventional receivers do. The simulation results show that the new block detection receiver is significantly better than the conventional non-block detection receivers in both additive white Gaussian noise and flat Rayleigh fading channels. A reduced-complexity algorithm for the block detection is also presented with significant complexity reduction and negligible performance degradation.
16012768
Continuous Optimization based-on Boosting Gaussian Mixture Model
A new estimation of distribution algorithm (EDA) based on Gaussian mixture model (GMM) is proposed, in which boosting, an efficient ensemble learning method, is adopted to estimate GMM. By boosting simple GMM with two components, it has the ability of learning the model structure and parameters automatically without any requirement for prior knowledge. Moreover, since boosting can be viewed as a gradient search for a good fit of some objective in function space, the new EDA is time efficient. A set of experiments is implemented to evaluate the efficiency and performance of the new algorithm. The results show that, with a relatively smaller population and less number of generations, the new algorithm can perform as well as compared EDAs in optimizing multimodal functions
37868959
A Method of Raster Data Mining Based on Multi Dimension Data Set
Spatial Data Mining can mine automatically or semi-automatically unknown, creditable, effective, integrative or schematic knowledge which can be understood from the increasingly complex spatial database and enhance the ability of interpreting data to generate useful knowledge. There are large amounts of data existing in the database of Government GIS. However, a great deal of the data is idle, which has caused a huge waste of data due to rarely effectively utilization in practice. It is very necessary to deal with the task of data mining based on Government GIS. In this paper, an example on land utilization and land cover in a certain region of Guizhou Province was presented to describe the course of spatial data mining. There into, a derived star-type model was used to organize the raster data to form multi dimension data set. Under these conditions, clustering method was utilized to carry out data mining aiming at the raster data. Based on corresponding analyses, users can find out which types of vegetation were suitable for being cultivated in this region by using related knowledge in a macroscopic view. Therefore, feasible service information could be provided to promote economic development in the region.
17891437
Call admission control for voice/data integrated cellular networks: performance analysis and comparative study
In this paper, we propose a new call admission control scheme called dual threshold bandwidth reservation, or DTBR scheme. The main novelty is that it builds upon a complete sharing approach, in which the channels in each cell are shared among the different traffic types and multiple thresholds are used to meet the specific quality-of-service (QoS) requirements. We present a detailed comparative study based on mathematical and simulation models, and quantitatively demonstrate that the DTBR is capable of providing the QoS guarantee for each type of traffic, while at the same time leading to much better channel efficiency. We further show that the DTBR scheme with elastic data service can offer both service guarantee and service differentiation for voice and data services, and enhance the bandwidth utilization.
10906433
A Practical Calibration Method for Multiple Cameras
In this paper, we propose a practical factorization- and-position based method for multiple cameras calibration. The method yields a simple calibration means for an arbitrary number of linear projective cameras while maintaining the handiness and flexibility of the original method. A freely moving planar pattern as a calibration object at a few different orientations is only required. All the cameras do not have to see this pattern at all orientations, and only reasonable overlap between camera subgroups is necessary. We divide these cameras into groups according to their positions and orientations first, and then calibrate each camera in the world coordinate system of its own group via a factorization-based method. Common view fields of planar pattern are used to estimate the Euclidean transformation between these world coordinate systems and represent all cameras in a same world coordinate system. Both the intrinsic and extrinsic parameters of cameras can be obtained in a uniform world coordinate system of accuracy to within a pixel.