175426
Incremental spectral clustering by efficiently updating the eigen-system
In recent years, the spectral clustering method has gained attentions because of its superior performance. To the best of our knowledge, the existing spectral clustering algorithms cannot incrementally update the clustering results given a small change of the data set. However, the capability of incrementally updating is essential to some applications such as websphere or blogsphere. Unlike the traditional stream data, these applications require incremental algorithms to handle not only insertion/deletion of data points but also similarity changes between existing points. In this paper, we extend the standard spectral clustering to such evolving data, by introducing the incidence vector/matrix to represent two kinds of dynamics in the same framework and by incrementally updating the eigen-system. Our incremental algorithm, initialized by a standard spectral clustering, continuously and efficiently updates the eigenvalue system and generates instant cluster labels, as the data set is evolving. The algorithm is applied to a blog data set. Compared with recomputation of the solution by the standard spectral clustering, it achieves similar accuracy but with much lower computational cost. It can discover not only the stable blog communities but also the evolution of the individual multi-topic blogs. The core technique of incrementally updating the eigenvalue system is a general algorithm and has a wide range of applications-as well as incremental spectral clustering-where dynamic graphs are involved. This demonstrates the wide applicability of our incremental algorithm.
2171158
Detecting video events based on action recognition in complex scenes using spatio-temporal descriptor
Event detection plays an essential role in video content analysis and remains a challenging open problem. In particular, the study on detecting human-related video events in complex scenes with both a crowd of people and dynamic motion is still limited. In this paper, we investigate detecting video events that involve elementary human actions, e.g. making cellphone call, putting an object down, and pointing to something, in complex scenes using a novel spatio-temporal descriptor based approach. A new spatio-temporal descriptor, which temporally integrates the statistics of a set of response maps of low-level features, e.g. image gradients and optical flows, in a space-time cube, is proposed to capture the characteristics of actions in terms of their appearance and motion patterns. Based on this kind of descriptors, the bag-of-words method is utilized to describe a human figure as a concise feature vector. Then, these features are employed to train SVM classifiers at multiple spatial pyramid levels to distinguish different actions. Finally, a Gaussian kernel based temporal filtering is conducted to segment the sequences of events from a video stream taking account of the temporal consistency of actions. The proposed approach is capable of tolerating spatial layout variations and local deformations of human actions due to diverse view angles and rough human figure alignment in complex scenes. Extensive experiments on the 50-hour video dataset of TRECVid 2008 event detection task demonstrate that our approach outperforms the well-known SIFT descriptor based methods and effectively detects video events in challenging real-world conditions.
2237682
Document clustering based on non-negative matrix factorization
In this paper, we propose a novel document clustering method based on the non-negative factorization of the term-document matrix of the given document corpus. In the latent semantic space derived by the non-negative matrix factorization (NMF), each axis captures the base topic of a particular document cluster, and each document is represented as an additive combination of the base topics. The cluster membership of each document can be easily determined by finding the base topic (the axis) with which the document has the largest projection value. Our experimental evaluations show that the proposed document clustering method surpasses the latent semantic indexing and the spectral clustering methods not only in the easy and reliable derivation of document clustering results, but also in document clustering accuracies.
15069022
An integrated baseball digest system using maximum entropy method
In this paper, we propose a novel system that is able to automatically detect and classify highlights from baseball game videos in TV broadcast. The digest system gives complete indexes of a baseball game which cover all of the status changes in a game. We achieve this by seamlessly integrating image, audio and speech clues using a maximum entropy based method. What distinguishes our system from previous ones is that we emphasize on the integration of multimedia features and the acquisition of domain knowledge through machine learning process. Integration of multimedia features is important because with the current state-of-the-art image and audio analysis techniques, most image and audio features we can extract from videos are very low level, and detecting/classifying sports game highlights based on features from single medium are doomed to yield poor performances. Acquiring domain knowledge through learning process is preferred over heuristic rules because machine learning process is more powerful for discovering and expressing domain knowledge. We perform extensive experiments on game videos including various stadiums, teams and broadcasted by different TV stations.
7153454
Temporal difference learning to detect unsafe system states
This paper proposes a general framework to detect unsafe states of a system whose basic realtime parameters are captured by multi-sensors. Our approach is to learn a danger level function which can be used to alert the users in advance of dangerous situations. The main challenge to this learning problem is the labelling issue, i.e., it is difficult to assign an objective danger level at each time step to the training data, except at the collapse points where a penalty can be assigned and at the successful ends where a certain reward can be assigned. In this paper, we treat the danger level as expected future reward (penalty is regarded as negative reward) and use temporal difference (TD) learning [2] to learn a function to approximate the expected future reward. The TD learning obtains the approximation by propagating the penalty/reward observable at collapse points or successful ends to the entire feature space following some constraints. Our approach is applied to, but not limited to, the application of monitoring of driving safety and the experimental results demonstrate the effectiveness of the approach.
9180515
Incremental Spectral Clustering With Application to Monitoring of Evolving Blog Communities
In recent years, spectral clustering method has gained attentions because of its superior performance compared to other traditional clustering algorithms such as K-means algorithm. The existing spectral clustering algorithms are all off-line algorithms, i.e., they can not incrementally update the clustering result given a small change of the data set. However, the capability of incrementally updating is essential to some applications such as real time monitoring of the evolving communities of websphere or blogsphere. Unlike traditional stream data, these applications require incremental algorithms to handle not only insertion/deletion of data points but also similarity changes between existing items. This paper extends the standard spectral clustering to such evolving data by introducing the incidence vector/matrix to represent two kinds of dynamics in the same framework and by incrementally updating the eigenvalue system. Our incremental algorithm, initialized by a standard spectral clustering, continuously and efficiently updates the eigenvalue system and generates instant cluster labels, as the data set is evolving. The algorithm is applied to a blog data set. Compared with recomputation of the solution by standard spectral clustering, it achieves similar accuracy but with much lower computational cost. Close inspection into the blog content shows that the incremental approach can discover not only the stable blog communities but also the evolution of the individual multi-topic blogs.
7039845
An Algorithm for Solving Scaled Total Least Squares Problems
In this paper, we present a rankrevealing two-sided orthogonal decomposition method for solving the STLS problem. An error analysis of the algorithm is given. Our numerical experiments show that this algorithm computes the STLS solution as accurate as the SVD method with less computation.
17935039
Data manipulation techniques to reduce phase change memory write energy
Due to its great scalability potential, phase change memory has become a topic of great current interest. However, high write energy consumption appears to be one of the biggest challenges to be tackled before phase change memory can be adopted as a mainstream memory technology. This paper presents architecture level technique to reduce phase change memory write energy consumption through data manipulations. Motivated by the fact that phase change memory read incurs much less energy than write and write of different value to a phase change memory cell incurs largely different energy, we present two memory write data manipulation techniques that can effectively reduce the overall memory write energy consumption. Their effectiveness has been demonstrated based on mathematical analysis and computer system simulation using phase change memory as the main memory in the computer memory hierarchy. Significant energy savings with up to more than 60% have been shown over a wide range of computer system benchmarks.
7346557
Predictive Control for Dynamic Resource Allocation in Enterprise Data Centers
It is challenging to reduce resource over-provisioning for enterprise applications while maintaining service level objectives (SLOs) due to their time-varying and stochastic workloads. In this paper, we study the effect of prediction on dynamic resource allocation to virtualized servers running enterprise applications. We present predictive controllers using three different prediction algorithms based on a standard auto-regressive (AR) model, a combined ANOVA-AR model, as well as a multi-pulse (MP) model. We compare the properties of the predictive controllers with an adaptive integral (I) controller designed in our earlier work on controlling relative utilization of resource containers. The controllers are evaluated in a hypothetical virtual server environment where we use the CPU utilization traces collected on 36 servers in an enterprise data center. Since these traces were collected in an open-loop environment, we use a simple queuing algorithm to simulate the closed-loop CPU usage under dynamic control of CPU allocation. We also study the controllers by emulating the utilization traces on a test bed where a Web server was hosted inside a Xen virtual machine. We compare the results of these controllers from all the servers and find that the MP-based predictive controller performed slightly better statistically than the other two predictive controllers. The ANOVA-AR-based approach is highly sensitive to the existence of periodic patterns in the trace, while the other three methods are not. In addition, all the three predictive schemes performed significantly better when the prediction error was accounted for using a feedback mechanism. The MP-based method also demonstrated an interesting self-learning behavior
30164221
Limited feedback design for MIMO broadcast channels with ARQ mechanism
We investigate the design of a limited feedback mechanism for multiple-input-multiple-output (MIMO) broadcast systems that are equipped with automatic repeat request (ARQ) capabilities. Given multiple antennas at the base-station (BS) and single antenna subscribers, we focus on maximizing the ergodic system sum capacity and propose an adaptive feedback bit allocation scheme under the constraint of total feedback bandwidth. Furthermore, we develop two efficient feedback allocation schemes based on asymptotic analysis. We present simulation results that verify the effectiveness of the proposed adaptive allocation schemes.
29892228
Bifurcations of traveling wave solutions for a class of the generalized Benjamin-Bona-Mahony equation
Abstract By using the theory of bifurcations of dynamical systems to a class of the generalized Benjamin–Bona–Mahony (GBBM) equation, the existence of solitary wave solutions and uncountably infinite, many smooth and non-smooth periodic wave solutions are obtained. It can be shown that the existence of singular curves in a traveling wave system is the reason why smooth waves converge to cusp waves, finally. When parameters are varied, under different parametric conditions, various sufficient conditions to guarantee the existence of the above solutions are given.