142 research outputs found
Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors
Promising results have been achieved in image classification problems by
exploiting the discriminative power of sparse representations for
classification (SRC). Recently, it has been shown that the use of
\emph{class-specific} spike-and-slab priors in conjunction with the
class-specific dictionaries from SRC is particularly effective in low training
scenarios. As a logical extension, we build on this framework for multitask
scenarios, wherein multiple representations of the same physical phenomena are
available. We experimentally demonstrate the benefits of mining joint
information from different camera views for multi-view face recognition.Comment: Accepted to International Conference in Image Processing (ICIP) 201
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
Adaptive Algorithm for Sparse Signal Recovery
Spike and slab priors play a key role in inducing sparsity for sparse signal
recovery. The use of such priors results in hard non-convex and mixed integer
programming problems. Most of the existing algorithms to solve the optimization
problems involve either simplifying assumptions, relaxations or high
computational expenses. We propose a new adaptive alternating direction method
of multipliers (AADMM) algorithm to directly solve the presented optimization
problem. The algorithm is based on the one-to-one mapping property of the
support and non-zero element of the signal. At each step of the algorithm, we
update the support by either adding an index to it or removing an index from it
and use the alternating direction method of multipliers to recover the signal
corresponding to the updated support. Experiments on synthetic data and
real-world images show that the proposed AADMM algorithm provides superior
performance and is computationally cheaper, compared to the recently developed
iterative convex refinement (ICR) algorithm
Multi-task and multi-kernel gaussian process dynamical systems
In this work, we propose a novel method for rectifying damaged motion sequences in an unsupervised manner. In order to achieve maximal accuracy, the proposed model takes advantage of three key properties of the data: their sequential nature, the redundancy that manifests itself among repetitions of the same task, and the potential of knowledge transfer across different tasks. In order to do so, we formulate a factor model consisting of Gaussian Process Dynamical Systems (GPDS), where each factor corresponds to a single basic pattern in time and is able to represent their sequential nature. Factors collectively form a dictionary of fundamental trajectories shared among all sequences, thus able to capture recurrent patterns within the same or across different tasks. We employ variational inference to learn directly from incomplete sequences and perform maximum a-posteriori (MAP) estimates of the missing values. We have evaluated our model with a number of motion datasets, including robotic and human motion capture data. We have compared our approach to well-established methods in the literature in terms of their reconstruction error and our results indicate significant accuracy improvement across different datasets and missing data ratios. Concluding, we investigate the performance benefits of the multi-task learning scenario and how this improvement relates to the extent of component sharing that takes place
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