Search for: All records

The concept of stimulus feature tuning isfundamental to neuroscience. Cortical neurons acquire their feature-tuning properties by learning from experience and using proxy signs of tentative features’ potential usefulness that come from the spatial and/or temporal context in which these features occur. According to this idea, local but ultimately behaviorally useful features should be the ones that are predictably related to other such features either preceding them in time or taking place side-by-side with them. Inspired by this idea, in this paper, deep neural networks are combined with Canonical Correlation Analysis (CCA) for feature extraction and the power of the features is demonstrated using unsupervised cross-modal prediction tasks. CCA is a multi-view feature extraction method that finds correlated features across multiple datasets (usually referred to as views or modalities). CCA finds linear transformations of each view such that the extracted principal components, or features, have a maximal mutual correlation. CCA is a linear method, and the features are computed by a weighted sum of each view's variables. Once the weights are learned, CCA can be applied to new examples and used for cross-modal prediction by inferring the target-view features of an example from its given variables in a source (query) view. To test the proposed method, it was applied to the unstructured CIFAR-100 dataset of 60,000 images categorized into 100 classes, which are further grouped into 20 superclasses and used to demonstrate the mining of image-tag correlations. CCA was performed on the outputs of three pre-trained CNNs: AlexNet, ResNet, and VGG. Taking advantage of the mutually correlated features extracted with CCA, a search for nearest neighbors was performed in the canonical subspace common to both the query and the target views to retrieve the most matching examples in the target view, which successfully predicted the superclass membership of the tested views without any supervised training.