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Learning Multiple Non-Linear Sub-Spaces using K-RBMsAuthors: Siddhartha Chandra,Shailesh Kumar, C V Jawahar Conference: International Conference on Computer Vision and Pattern Recognition, 23-28 June. 2013, Oregon, USA. Date: 2013-06-23 Report no: IIIT/TR/2013/62 AbstractUnderstanding the nature of data is the key to build- ing good representations. In domains such as natural im- ages, the data comes from very complex distributions which are hard to capture. Feature learning intends to discover or best approximate these underlying distributions and use their knowledge to weed out irrelevant information, pre- serving most of the relevant information. Feature learning can thus be seen as a form of dimensionality reduction. In this paper, we describe a feature learning scheme for nat- ural images. We hypothesize that image patches do not all come from the same distribution, they lie in multiple non- linear subspaces. We propose a framework that uses K Restricted Boltzmann Machines ( K-RBM S ) to learn mul- tiple non-linear subspaces in the raw image space. Pro- jections of the image patches into these subspaces gives us features, which we use to build image representations. Our algorithm solves the coupled problem of finding the right non-linear subspaces in the input space and associ- ating image patches with those subspaces in an iterative EM like algorithm to minimize the overall reconstruction error. Extensive empirical results over several popular im - age classification datasets show that representations base d on our framework outperform the traditional feature repre- sentations such as the SIFT based Bag-of-Words (BoW) and convolutional deep belief networks. Full paper: pdf Centre for Visual Information Technology |
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