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The kernel trick becomes a burden for some machine learning tasks such as dictionary learning, where a huge amount of training samples are needed, making the kernel matrix gigantic and infeasible to store or process. In this work, we propose to alleviate this problem and achieve Gaussian RBF kernel expansion explicitly for dictionary learning using Fastfood transform, which is an approximation of...
This paper presents a parallel motion estimation algorithm on Graphics Processing Units (GPU) with a GPU-based fast Coding Unit (CU) splitting mechanism for speeding up the execution speed of High Efficiency Video Coding (HEVC). Parallel motion estimation algorithms only offer motion vectors to HEVC encoder, but CU splitting decision in HEVC still needs more information to speed up the encoder. Therefore,...
Whole-system data provenance provides deep insight into the processing of data on a system, including detecting data integrity attacks. The downside to systems that collect whole-system data provenance is the sheer volume of data that is generated under many heavy workloads. In order to make provenance metadata useful, it must be stored somewhere where it can be queried. This problem becomes even...
This paper presents a polar code design for block fading channels when no channel state information is available at the transmitter, which involves that the frozen bits cannot be changed dynamically with the fading realizations. An outer parallel code is concatenated with an inner polarization kernel that changes the properties of the block fading channel. The rate-splitting between the parallel outer...
In contrast to the network coding problem wherein the sinks in a network demand subsets of the source messages, in a network computation problem the sinks demand functions of the source messages. Similarly, in the functional index coding problem, the side information and demands of the clients include disjoint sets of functions of the information messages held by the transmitter instead of disjoint...
Action recognition has been one of the most popular fields of computer vision. This paper presents a novel approach to action recognition problem using the dimension reduction method, local fisher discriminant analysis, to reduce the dimension of feature descriptors as the preprocessing step after feature extraction. We propose to use sparse matrix and randomized kd-tree to modify and accelerate the...
Very-long-instruction-word (VLIW) architectures are widely adopted in high-performance and low-power digital signal processors (DSP) due to their simplicity from extensive software optimizations. However, their poor code density (usually > 2× code size for a given application) and corresponding instruction accesses can overwhelm the energy savings on DSP datapaths. This paper presents variable-length...
With the advance of 3-dimensional sensing devices, the in-air handwriting, as a more natural way for human and computer interaction, is being developed by the UCAS-CVMT Lab. Compared with the conventional handwritten Chinese characters generated by touching, it is more challenging to accurately recognize them due to unconstrained one-stroke writing style. This paper presents two recognizers to address...
Kernel dictionary learning method recently has become a very effective strategy for object recognition. However, it encounters large storage and calculation challenges when there is a large amount of training data. In this paper, we propose a new optimization model to simultaneously perform prototype selection and kernel dictionary learning. This model can be easily used for online kernel dictionary...
This paper presents a unified Non-local Spectral-spatial Centralized Sparse Representation (NL-CSR) model for the hyper-spectral image classification. The proposed model integrates local sparsity and non-local mean centralized induced sparsity. To achieve rich spectral-spatial information, the centralized sparsity enforces the sparse coding vector towards its non-local structural self-similar mean...
In this paper, we demonstrate nonlinear features extracted by deep neural network have better results in the task of dictionary learning. A nonlinear dictionary learning model is constructed and the optimization algorithm is developed. In the learning algorithm, we use the deep neural network to convey raw samples to feature space and learn a nonlinear dictionary. The extensive experimental results...
We study the problem of learning lexicographic preferences on multiattribute domains, and propose Rankdom Forests as a compact way to express preferences in learning to rank scenarios. We start generalizing Conditional Lexicographic Preference Trees by introducing multiple kernels in order to handle non-categorical attributes. Then, we define a learning strategy for inferring lexicographic rankers...
Support vector machine (SVM) is a powerful tool for classification and regression problems, however, its time and space complexities make it unsuitable for large datasets. In this paper, we present GeneticSVM, an evolutionary computing based distributed approach to find optimal solution of quadratic programming (QP) for kernel support vector machine. In Ge-neticSVM, novel encoding method and crossover...
Often in real-world applications such as web page categorization, automatic image annotations and protein function prediction, each instance is associated with multiple labels (categories) simultaneously. In addition, due to the labeling cost one usually deals with a large amount of unlabeled data while the fraction of labeled data points will typically be small. In this paper, we propose a multi-label...
The problem of place recognition is central to robot navigation. The robot needs to be able to recognize or at least to be able to estimate the likelihood that it has been at a place before when it has returned to a previously visited place. We cast the place recognition problem as one of classifying among multiple linear regression models, and argue that new theory from sparse signal representation...
The framework of the ScSPM (Spatial Pyramid matching method using Sparse Coding) model is concise, but a good performance in scene classification is achieved. However, its performance can not be significantly improved duo to the limited discriminative power of the SIFT descriptors. To address the problem, covariance matrices as region descriptors are introduced to incorporate with the SIFTs. For computing...
Hashing learning has attracted increasing attention these years with the explosive increase of data. The hashing learning can be divided into two steps. Firstly, obtain the low dimensional representation of the original data. Secondly, quantize the real number vector of the low dimensional representation of each data point and map them to binary codes. Most of the existing methods measure the original...
While feasibility and obtaining a solution of a given network coding problem are well studied, the decoding procedure and complexity have not garnered much attention. We consider the decoding problem in a network wherein the sources generate multiple messages and the sink nodes demand some or all of the source messages. We consider both linear and non-linear network codes over a finite field and propose...
The logarithmic number system (LNS) has always been an interesting alternative for floating point calculations since the implementation of several arithmetic operations such as divisions, exponentiations and square-roots, which are required for computationally intensive nonlinear functions, is greatly simplified in the logarithmic space. However, additions and subtractions become nonlinear operations...
This paper explores the learning of speaker dictionary encoding class-specific and class-common information to enhance the discriminative ability in context of sparse representation based speaker verification (SV). Typically, the KSVD learned dictionary is employed that is well suited for minimizing the representation error, but is not optimized for classification purpose. The work is motivated by...
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