We present an efficient method for learning part-based object class models. The models include location and scale relations between parts, as well as part appearance. Models are learnt from raw object and background i...
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We argue that when objects are characterized by many attributes, clustering them on the basis of a relatively small random subset of these attributes can capture information on the unobserved attributes as well. Moreo...
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ISBN:
(纸本)9780262232531
We argue that when objects are characterized by many attributes, clustering them on the basis of a relatively small random subset of these attributes can capture information on the unobserved attributes as well. Moreover, we show that under mild technical conditions, clustering the objects on the basis of such a random subset performs almost as well as clustering with the full attribute set. We prove a finite sample generalization theorems for this novel learning scheme that extends analogous results from the supervised learning setting. The scheme is demonstrated for collaborative filtering of users with movies rating as attributes.
We present an algorithm for learning a quadratic Gaussian metric (Mahalanobis distance) for use in classification tasks. Our method relies on the simple geometric intuition that a good metric is one under which points...
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ISBN:
(纸本)9780262232531
We present an algorithm for learning a quadratic Gaussian metric (Mahalanobis distance) for use in classification tasks. Our method relies on the simple geometric intuition that a good metric is one under which points in the same class are simultaneously near each other and far from points in the other classes. We construct a convex optimization problem whose solution generates such a metric by trying to collapse all examples in the same class to a single point and push examples in other classes infinitely far away. We show that when the metric we learn is used in simple classifiers, it yields substantial improvements over standard alternatives on a variety of problems. We also discuss how the learned metric may be used to obtain a compact low dimensional feature representation of the original input space, allowing more efficient classification with very little reduction in performance.
Grid services provide an important abstract layer on top of heterogeneous components (hardware and software) that take part into a grid environment. We are developing a data grid service prototype that aims at providi...
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We devise and experiment with a dynamical kernel-based system for tracking hand movements from neural activity. The state of the system corresponds to the hand location, velocity, and acceleration, while the system...
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ISBN:
(纸本)0262195348
We devise and experiment with a dynamical kernel-based system for tracking hand movements from neural activity. The state of the system corresponds to the hand location, velocity, and acceleration, while the system's input are the instantaneous spike rates. The system's state dynamics is defined as a combination of a linear mapping from the previous estimated state and a kernel-based mapping tailored for modeling neural activities. In contrast to generative models, the activity-to-state mapping is learned using discriminative methods by minimizing a noise-robust loss function. We use this approach to predict hand trajectories on the basis of neural activity in motor cortex of behaving monkeys and find that the proposed approach is more accurate than both a static approach based on support vector regression and the Kalman filter.
Training a learning algorithm is a costly task. A major goal of active learning is to reduce this cost. In this paper we introduce a new algorithm, KQBC, which is capable of actively learning large scale problems by u...
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ISBN:
(纸本)9780262232531
Training a learning algorithm is a costly task. A major goal of active learning is to reduce this cost. In this paper we introduce a new algorithm, KQBC, which is capable of actively learning large scale problems by using selective sampling. The algorithm overcomes the costly sampling step of the well known Query By Committee (QBC) algorithm by projecting onto a low dimensional space. KQBC also enables the use of kernels, providing a simple way of extending QBC to the non-linear scenario. Sampling the low dimension space is done using the hit and run random walk. We demonstrate the success of this novel algorithm by applying it to both artificial and a real world problems.
A novel scheme is proposed by integrating pattern module's method based on strict mathematical model into the subdomain meshing stage of multi-subdomain methods. The scheme is capable of nicely overcoming two draw...
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A novel scheme is proposed by integrating pattern module's method based on strict mathematical model into the subdomain meshing stage of multi-subdomain methods. The scheme is capable of nicely overcoming two drawbacks of current subdomain meshing algorithms: over-rigorous subdomain definition and over-complex generation rule. To get a quality guaranteed pattern module scheme, two existing schemes are compared, and some heuristic principles are given to provide guidance. A degenerate case of the preferred scheme is analyzed in detail, and its corresponding solution is given. Then a robust and quality guaranteed pattern module scheme is constructed. Finally, meshes for several sample geometries are presented to illustrate the versatility and validity of the scheme.
Spike sorting involves clustering spike trains recorded by a microelectrode according to the source neuron. It is a complicated problem, which requires a lot of human labor, partly due to the non-stationary nature of ...
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ISBN:
(纸本)0262195348
Spike sorting involves clustering spike trains recorded by a microelectrode according to the source neuron. It is a complicated problem, which requires a lot of human labor, partly due to the non-stationary nature of the data. We propose an automated technique for the clustering of non-stationary Gaussian sources in a Bayesian framework. At a first search stage, data is divided into short time frames and candidate descriptions of the data as a mixture of Gaussians are computed for each frame. At a second stage transition probabilities between candidate mixtures are computed, and a globally optimal clustering is found as the MAP solution of the resulting probabilistic model. Transition probabilities are computed using local stationarity assumptions and are based on a Gaussian version of the Jensen-Shannon divergence. The method was applied to several recordings. The performance appeared almost indistinguishable from humans in a wide range of scenarios, including movement, merges, and splits of clusters.
In this paper, we present our efforts to parallelize an unstructured quadrilateral mesh generator. Its serial version is based on the divider-and-conquer idea, and mainly includes two stages, i.e. geometry decompositi...
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In this paper, we present our efforts to parallelize an unstructured quadrilateral mesh generator. Its serial version is based on the divider-and-conquer idea, and mainly includes two stages, i.e. geometry decomposition and mesh generation. Both stages are parallelized separately. A highly efficient fine-grain level parallel scheme is presented to parallelize the stage of geometry decomposition. A SubDomain Graph (SDG), which represents the connections of subdomains, is constructed. The task of parallel mesh generation is then reduced to that of the SDG partitioning. Since the number of elements in subdomains could be pre-computed before meshing, a static load balancing scheme to partition the SDG performs well with the aid of Metis tools. Numerical results show that scalable timing performance could be achieved by using the parallel mesh generator with resulting meshes nicely partitioned among processors, which enables a fast parallel simulation environment by eliminating the traditional I/O-busy process of mesh repartitioning.
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