262,344 research outputs found
Multiclass latent locally linear support vector machines
Kernelized Support Vector Machines (SVM) have gained the status of off-the-shelf classifiers, able to deliver state of the art performance on almost any problem. Still, their practical use is constrained by their computational and memory complexity, which grows super-linearly with the number of training samples. In order to retain the low training and testing complexity of linear classifiers and the exibility of non linear ones, a growing, promising alternative is represented by methods that learn non-linear classifiers through local combinations of linear ones. In this paper we propose a new multi class local classifier, based on a latent SVM formulation. The proposed classifier makes use of a set of linear models that are linearly combined using sample and class specific weights. Thanks to the latent formulation, the combination coefficients are modeled as latent variables. We allow soft combinations and we provide a closed-form solution for their estimation, resulting in an efficient prediction rule. This novel formulation allows to learn in a principled way the sample specific weights and the linear classifiers, in a unique optimization problem, using a CCCP optimization procedure. Extensive experiments on ten standard UCI machine learning datasets, one large binary dataset, three character and digit recognition databases, and a visual place categorization dataset show the power of the proposed approach
Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines
Tackling pattern recognition problems in areas such as computer vision,
bioinformatics, speech or text recognition is often done best by taking into
account task-specific statistical relations between output variables. In
structured prediction, this internal structure is used to predict multiple
outputs simultaneously, leading to more accurate and coherent predictions.
Structural support vector machines (SSVMs) are nonprobabilistic models that
optimize a joint input-output function through margin-based learning. Because
SSVMs generally disregard the interplay between unary and interaction factors
during the training phase, final parameters are suboptimal. Moreover, its
factors are often restricted to linear combinations of input features, limiting
its generalization power. To improve prediction accuracy, this paper proposes:
(i) Joint inference and learning by integration of back-propagation and
loss-augmented inference in SSVM subgradient descent; (ii) Extending SSVM
factors to neural networks that form highly nonlinear functions of input
features. Image segmentation benchmark results demonstrate improvements over
conventional SSVM training methods in terms of accuracy, highlighting the
feasibility of end-to-end SSVM training with neural factors
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
On The Effect of Hyperedge Weights On Hypergraph Learning
Hypergraph is a powerful representation in several computer vision, machine
learning and pattern recognition problems. In the last decade, many researchers
have been keen to develop different hypergraph models. In contrast, no much
attention has been paid to the design of hyperedge weights. However, many
studies on pairwise graphs show that the choice of edge weight can
significantly influence the performances of such graph algorithms. We argue
that this also applies to hypegraphs. In this paper, we empirically discuss the
influence of hyperedge weight on hypegraph learning via proposing three novel
hyperedge weights from the perspectives of geometry, multivariate statistical
analysis and linear regression. Extensive experiments on ORL, COIL20, JAFFE,
Sheffield, Scene15 and Caltech256 databases verify our hypothesis. Similar to
graph learning, several representative hyperedge weighting schemes can be
concluded by our experimental studies. Moreover, the experiments also
demonstrate that the combinations of such weighting schemes and conventional
hypergraph models can get very promising classification and clustering
performances in comparison with some recent state-of-the-art algorithms
Eigen-spectrograms: an interpretable feature space for bearing fault diagnosis based on artificial intelligence and image processing
The Intelligent Fault Diagnosis of rotating machinery proposes some
captivating challenges in light of the imminent big data era. Although results
achieved by artificial intelligence and deep learning constantly improve, this
field is characterized by several open issues. Models' interpretation is still
buried under the foundations of data driven science, thus requiring attention
to the development of new opportunities also for machine learning theories.
This study proposes a machine learning diagnosis model, based on intelligent
spectrogram recognition, via image processing. The approach is characterized by
the introduction of the eigen-spectrograms and randomized linear algebra in
fault diagnosis. The eigen-spectrograms hierarchically display inherent
structures underlying spectrogram images. Also, different combinations of
eigen-spectrograms are expected to describe multiple machine health states.
Randomized algebra and eigen-spectrograms enable the construction of a
significant feature space, which nonetheless emerges as a viable device to
explore models' interpretations. The computational efficiency of randomized
approaches further collocates this methodology in the big data perspective and
provides new reading keys of well-established statistical learning theories,
such as the Support Vector Machine (SVM). The conjunction of randomized algebra
and Support Vector Machine for spectrogram recognition shows to be extremely
accurate and efficient as compared to state of the art results.Comment: 14 pages, 13 figure
Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks
Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of traitâmatching for determining species interactions, however, vary significantly among different types of ecological networks.
Here, we show that ambiguity among empirical traitâmatching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naĂŻve Bayes, and kâNearestâNeighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions.
We found that the best ML models can successfully predict species interactions in plantâpollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible traitâmatching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plantâpollinator database and inferred ecologically plausible traitâmatching rules for a plantâhummingbird network from Costa Rica, without any prior assumptions about the system.
We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition
Towards Inferring Mechanical Lock Combinations using Wrist-Wearables as a Side-Channel
Wrist-wearables such as smartwatches and fitness bands are equipped with a
variety of high-precision sensors that support novel contextual and
activity-based applications. The presence of a diverse set of on-board sensors,
however, also expose an additional attack surface which, if not adequately
protected, could be potentially exploited to leak private user information. In
this paper, we investigate the feasibility of a new attack that takes advantage
of a wrist-wearable's motion sensors to infer input on mechanical devices
typically used to secure physical access, for example, combination locks. We
outline an inference framework that attempts to infer a lock's unlock
combination from the wrist motion captured by a smartwatch's gyroscope sensor,
and uses a probabilistic model to produce a ranked list of likely unlock
combinations. We conduct a thorough empirical evaluation of the proposed
framework by employing unlocking-related motion data collected from human
subject participants in a variety of controlled and realistic settings.
Evaluation results from these experiments demonstrate that motion data from
wrist-wearables can be effectively employed as a side-channel to significantly
reduce the unlock combination search-space of commonly found combination locks,
thus compromising the physical security provided by these locks
- âŠ