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Deep networks like the convolutional neural network and its variants usually learn hierarchical features from labeled images, which is very expensive to obtain. How can we find an unsupervised way to effectively extract deep and abstract features from images without annotations? Even from large qualities of images with noise? In this paper, we propose a robust deep neural network, named as stacked...
The iterative closest point (ICP) algorithm is fast and accurate for rigid point set registration, but it works badly when there are many outliers and noises in the point sets. This paper instead proposes a novel method based on the ICP algorithm to deal with this problem. Firstly, correntropy is introduced into the rigid registration problem and then a new energy function based on maximum correntropy...
Feature selection is an effective technique for dimensionality reduction to get the most useful information from huge raw data. Many spectral feature selection algorithms have been proposed to address the unsupervised feature selection problem, but most of them fail to pay attention to the noises induced during the feature selection process. In this paper, we not only consider the feature structural...
Time series prediction relies on past data points to make robust predictions. The span of past data points is important for some applications since prediction will not be possible unless the minimal timespan of the data points is available. This is a problem for cyclone wind-intensity prediction, where prediction needs to be made as a cyclone is identified. This paper presents an empirical study on...
Smart video analysis is attracting increasing attention with the pervasive use of surveillance camera. In this paper, we address video anomaly detection by Uniform Local Gradient Pattern based Optical Flow (ULGP-OF) descriptor and one-class extreme learning machine (OCELM). Using the proposed ULGP-OF descriptor, we naturally combine the robust 2D image texture descriptor LGP with video optical flow...
The correntropy provides a robust criterion for outlier-insensitive machine learning, and its maximisation has been increasingly investigated in signal and image processing. In this paper, we investigate the problem of unmixing hyperspectral images, namely decomposing each pixel/spectrum of a given image as a linear combination of other pixels/spectra called endmembers. The coefficients of the combination...
We introduce a novel variation on the well-known Matching Pursuit (MP) algorithm. In particular, the sparse approximation problem is solved in a greedy scheme using estimated higher-order statistics as similarity measures instead of the somehow limited second-order statistics that perform optimally only under Gaussian assumptions. This is conveyed via the generalized correntropy (GC) function instead...
Survival information potential (SIP) is defined by the survival distribution function instead of the probability density function (PDF) of a random variable. SIP can be used as a risk function equipped with learning error compensation ability while this SIP based risk function does not involve the estimation of PDF. This is desirable for a robust learning application in view of the error compensation...
The traditional affine iterative closest point (ICP) algorithm is fast and accuracy for affine registration of point sets, but it performs worse when the point sets with large outliers. This paper introduces a novel algorithm based on correntropy for affine registration of point sets with outliers. First, a novel objective function is proposed by introducing the maximum correntropy criterion (MCC)...
The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector)...
We propose a Joint nuclear norm based nonlinear Manifold Learning through linear embedding with Classification, called JMLC. By including a feature approximation error into the existing nonlinear manifold learning framework to correlate manifold features with embedded features by a linear projection, the learnt projection can handle the outside points efficiently by embedding. Besides, to encode the...
In this paper, we present a new and effective dimensionality reduction method called locality sparsity preserving projections (LSPP). Locality preserving projections (LPP) and sparsity preserving projections (SPP) only focus on an aspect of local structure and sparse reconstructive information of the dataset, respectively. The proposed method integrates the sparse reconstructive information and local...
Biologically inspired episodic memory is able to store time sequential events, and to recall all of them from partial information. Because of the advantages of episodic memory, the biological concepts of episodic memory have been utilized to many applications. In this research, we propose a new memory model, called Deep ART (Adaptive Resonance Theory), to make a robust memory system for learning episodic...
Through multiple levels of abstraction, deep learning takes advantage of multiple layers models to find the complicated structure and learn the high level representations of data. In recent years, deep learning has made great progress in object detection, speech recognition, and many other domains. The robustness of learning systems with deep architectures is however rarely studied and needs further...
A wavelet neural network (WNN)-based robust total-sliding-mode control scheme is developed for the synchronization of uncertain chaotic systems. The proposed control system offers a design method to drive the state trajectory to track a desired trajectory, and it is comprised of an adaptive WNN controller and a robust compensator. The adaptive WNN controller acts as the principal tracking controller,...
Adversarial examples have raised questions regarding the robustness and security of deep neural networks. In this work we formalize the problem of adversarial images given a pretrained classifier, showing that even in the linear case the resulting optimization problem is nonconvex. We generate adversarial images using shallow and deep classifiers on the MNIST and ImageNet datasets. We probe the pixel...
This paper explores modifications to a feedforward five-layer spiking convolutional network (SCN) of the ventral visual stream [Masquelier, T., Thorpe, S., Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Computational Biology, 3(2), 247–257]. The original model showed that a spike-timing-dependent plasticity (STDP) learning algorithm embedded in an appropriately...
In this paper, a class of second-order nonlinear time-delayed multiagent systems with disturbance is investigated. In order to improve the adaptivity, neural networks are used to learn the unknown dynamics. Then, by utilizing Lyapunov-Krasovskii functional, time delays can be eliminated. Moreover, a robustifying term is introduced to constrain external disturbance. With divide-and-conquer idea, the...
We propose an effective subspace selection scheme as a post-processing step to improve results obtained by sparse subspace clustering (SSC). Our method starts by the computation of stable subspaces using a novel random sampling scheme. Thus constructed preliminary subspaces are used to identify the initially incorrectly clustered data points and then to reassign them to more suitable clusters based...
Owing to their universal approximation capability and online learning manner, kernel adaptive filters have been widely used in nonlinear systems modeling. Under Gaussian assumption, traditional kernel adaptive algorithms utilize the well-known mean square error(MSE) as a cost function to get optimal solutions. For non-Gaussian situations, MSE will not properly represent the statistics of the error,...
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