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Extracting useful knowledge from data streams is problematic, mainly due to changes in their data distribution, a phenomenon named concept drift. Recently, studies have shown that most of existing algorithms for learning from data streams do not encompass techniques for a specific kind of drift: feature drifts. Feature drifts occur when features become, or cease to be, relevant to the learning task...
The word embedding models are capable of capturing the semantic content of the textual words. The process of extracting a set of word embedding vectors from a text document is similar to the feature extraction step of the Bag-of-Features pipeline, which is usually used in computer vision tasks. That gives rise to the Bag-of-Embedded Words (BoEW) model. In this paper a novel learning technique that...
Large scale biometrics projects rely on capturing images/signal from multiple sensors. For example, in India's Aadhaar project, multiple fingerprint sensors of different make and model are used for data collection. Similarly, in law enforcement applications, different agencies use different fingerprint sensors. These scenarios cause two potential problems: (i) sensor inter-operability and (ii) protecting/recording...
We propose a general framework for information theoretic feature selection based on the integer programming. Filter feature selection methods usually rely on a greedy forward or backward selection heuristic to find a satisfactory set of features, as the exact search is a combinatorial problem. We formulate the existing filter information theoretic criteria into an integer programming problem, and...
This paper considers the problem of material recognition. Motivated by observation of close interconnections between material and object recognition, we study how to select and integrate multiple features obtained by different models of Convolutional Neural Networks (CNNs) trained in a transfer learning setting. To be specific, we first compute activations of features using representations on images...
One of the most important cues for human communication is the interpretation of facial expressions. We present a novel computer vision approach for Action Unit (AU) recognition based upon a deep learning framework combined with a semantic context model. We introduce a new convolutional neural network training loss specific to AU intensity that utilizes a binned cross entropy method to fine-tune an...
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