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The problem of combining multi-modal features which extract from characteristics of given Cloud Computing Servers in the pattern recognition system is well known difficult. This paper addresses a novel efficient technique for normalizing sets of features which are highly multi-modal in nature, so as to allow them to be incorporated from a multi-dimensional feature distribution space. The intend system...
Over the last decades, machine learning techniques have been an important asset for detecting nonlinear relations in data. In particular, one-class classification has been very popular in many fields, specifically in applications where the available data refer to a unique class only. In this paper, we propose a sparse approach for one-class classification problems. We define the one-class by the hypersphere...
Now, cross-modal retrieval similarity on multimedia with texts and images have attracted scholars' more and more attention. The difficulty of cross-modal retrieval is how to effectively construct correlation between multi-modal heterogeneous data. According to canonical correlation analysis, most existing cross-modal methods embed the heterogeneous data into a joint abstraction space by linear projections...
Identifying spatial patterns of geographic entities such as retail stores is important in city for understanding how they behave. The pattern formed by the distribution of points can be measured by some quantitative methods. In Big Data era, the data sets for spatial patterns analysis are various including traditional street network data and points of interest (POIs) data in LBS (Location based services)...
In this paper, we address the problem of human pose estimation through a novel articulated Gaussian kernel correlation function which is applied to human pose tracking from a single depth sensor. We first derive a unified Gaussian kernel correlation that can generalize the previous Sum-of-Gaussians (SoG)-based methods for the similarity measure between a template and the observation. Furthermore,...
We propose a new cross-modal correlation learning framework which boosts the performance of correlation learning models using the hyperlink information. First, we design a neighborhood selection paradigm using the hyperlink structure and content similarities to identify a set of semantically related documents for each multi-modal document in both training and testing stage. Based on the neighborhood...
Linear search arises in many application domains. The problem of linear search over multiple sequences in order to identify one sequence with a desired statistical feature is considered. The quickest linear search optimizes a balance between two opposing performance measures, one being the delay in detecting a desirable sequence, and the other one being the quality of the decision. The existing approaches...
Autism is a neuro-developmental disorder that retards the normal cognitive development of an affected person. It is prevalent in children below the age of five and is generally identified through the symptoms exhibited by them while they interact with the environment. This work focuses on the extraction of texture features for autistic and control subjects and validation is done using the neural classifiers,...
Diagnosis of disease is done by physical examination of patient by physician. For internal observation physician requires help of sonography, MRI, pathological tests reports etc. In Ayurveda Nadi-Pariksha (pulse examination) is used for making the diagnosis. It uses pulse signal sensed at radial artery on wrist below the thumb for diagnosis manually. The pulse signal contains very useful information...
In this paper, first we propose a new approach for mathematical multiple criteria decision making (MCDM) methods using information theoretic measures, entropy and divergence. Using the concept of entropy, we determine the impact of each criterion in decision making process. The Shannon's entropy has been previously employed for this purpose. In this paper we use Renyi's entropy and the concept of...
Fingerprint minutiae distribution plays a critical role in studies such as fingerprint individuality for strengthening the scientific validity of fingerprint evidence and generating synthetic fingerprints for large-scale system evaluations. Fingerprint minutiae are not uniformly distributed as once assumed. Spatial inhomogeneity has been found in minutiae distribution, yet it is not clear what underlies...
This paper describes Principal Component Analysis (PCA) used for pre-processing data before training artificial neural networks. Interpretation of the pre-processed data is attempted for time-series data and it is argued that the principal components extracted by linear PCA have an interpretation in the frequency domain. Results are cited showing that a frequency domain interpretation of the eigenvalues...
The array output for a distributed source can be approximated by the superposition of the array response to a large number of closely spaced point sources. In the limit, a distributed source corresponds to an infinite number of point sources. In this approximation, the number of free parameters increases with the number of point sources. In this paper, we show that if the point sources (approximation...
Cortical parcellation of the human brain typically serves as a basis for higher-level analyses such as connectivity analysis and investigation of brain network properties. Inferences drawn from such analyses can be significantly confounded if the brain parcels are inaccurate. In this paper, we propose a novel affinity matrix structure based on multiple kernel density estimation for cortical parcellation...
Variation of patterns in signal can be represented by the covariance structure of vectors or its eigensubspace. When information of the pattern variation is available, representation by the covariance matrix or the eigensubspace is useful for feature extraction and classification compared with standard vector or matrix representations.
Generalized convolution and correlation theorems for the Wigner-Ville distribution (transform) associated with linear canonical transform (WVD-LCT) are established. The proposed theorems are modified forms of the convolution and correlation theorems of the linear canonical transform and classical Wigner-Ville distribution.
This paper forms a part of a series of recent studies we have undertaken, where the problem of nonlinear signal modelling is examined. We assume that an observed "output" signal is derived from a Volterra filter that is driven by a Gaussian input. Both the filter parameters and the input signal are unknown and therefore the problem can be classified as blind or unsupervised in nature. In...
Information obtained from redness grading can assist clinician for diagnosis and in making clinical decision. This research work aims to mimic human perception of fibrovascular redness using features extracted from color entropy. Gaussian process regression with the radial basis function kernel has been employed to fuse relevant features and established the model of redness perception. In this paper,...
In this paper, we highlight a design of Gaussian kernels for online model selection by the multikernel adaptive filtering approach. In the typical multikernel adaptive filtering, the maximum value that each kernel function can take is one. This means that, if one employs multiple Gaussian kernels with multiple variances, the one with the largest variance would become dominant in the kernelized input...
In this paper, we propose a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and multiple kernel learning (MKL) based multi-modal affect recognition scheme (LSTM-MKL). It takes the LSTM-RNN advantage to model the long range dependencies between successive observations, and uses the MKL power to model the non-linear correlations between the inputs and outputs. For each of the affect dimensions...
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