The objective of this letter is to propose a novel computational method to learn the state of an appliance (ON / OFF) given the aggregate power consumption recorded by the smart-meter. We formulate a multi-label classification problem where the classes correspond to the appliances. The proposed approach is based on our recently introduced framework of convolutional transform learning. We propose a deep supervised version of it relying on an original multi-label cost. Comparisons with state-of-the-art techniques show that our proposed method improves over the benchmarks on popular non-intrusive load monitoring datasets.

Curbing hate speech is undoubtedly a major challenge for online microblogging platforms like Twitter. While there have been studies around hate speech detection, it is not clear how hate speech finds its way into an online discussion. It is important for a content moderator to not only identify which tweet is hateful but also to predict which tweet will be responsible for accumulating hate speech. This would help in prioritizing tweets that need constant monitoring. Our analysis reveals that for hate speech to manifest in an ongoing discussion, the source tweet may not necessarily be hateful; rather, there are plenty of such non-hateful tweets which gradually invoke hateful replies, resulting in the entire reply threads becoming provocative.

This work proposes a new approach for dynamical modeling; we call it sequential transform learning. This is loosely based on the transform (analysis dictionary) learning formulation. This is the first work on this topic. Transform learning, was originally developed for static problems; we modify it to model dynamical systems by introducing a feedback loop. The learnt transform coefficients for the t th instant are fed back along with the t + 1st sample, thereby establishing a Markovian relationship. Furthermore, the formulation is made supervised by the label consistency cost. Our approach keeps the best of two worlds, marrying the interpretability and uncertainty measure of signal processing with the function approximation ability of neural networks. We have carried out experiments on one of the most challenging problems in dynamical modeling - stock forecasting. Benchmarking with the state-of-the-art has shown that our method excels over the rest.

Subspace clustering assumes that the data is separable into separate subspaces. Such a simple assumption, does not always hold. We assume that, even if the raw data is not separable into subspaces, one can learn a representation (transform coefficients) such that the learnt representation is separable into subspaces. To achieve the intended goal, we embed subspace clustering techniques (locally linear manifold clustering, sparse subspace clustering and low rank representation) into transform learning. The entire formulation is jointly learnt; giving rise to a new class of methods called transformed subspace clustering (TSC). In order to account for non-linearity, kernelized extensions of TSC are also proposed. To test the performance of the proposed techniques, benchmarking is performed on image clustering and document clustering datasets. Comparison with state-of-the-art clustering techniques shows that our formulation improves upon them.

This work addresses the problem of extracting deeply learned features directly from compressive measurements. There has been no work in this area, existing deep learning tools only give good results when applied on the full signal (that too usually after pre-processing). These techniques require the signal to be reconstructed first. In this work we show that by learning directly from the compressed domain, considerably better results can be obtained. This work extends the recently proposed framework of deep matrix factorization in combination with blind compressed sensing, hence the term 'deep blind compressed sensing'. Simulation experiments have been carried out on imaging via single pixel camera, under-sampled biomedical signals (arising in wireless body area network) and compressive hyperspectral imaging. In all cases, the superiority of our proposed deep blind compressed sensing can be envisaged.

Currently there are two predominant ways to train deep neural networks. The first one uses restricted Boltzmann machine (RBM) and the second one auto encoders. RBMs are stacked in layers to form deep belief network (DBN), the final representation layer is attached to the target to complete the deep neural network. Auto encoders are nested one inside the other to form stacked auto encoders, once the stcaked auto encoder is learnt the decoder portion is detached and the target attached to the deepest layer of the encoder to form the deep neural network. This work proposes a new approach to train deep neural networks using dictionary learning as the basic building block, the idea is to use the features from the shallower layer as inputs for training the next deeper layer. One can use any type of dictionary learning (unsupervised, supervised, discriminative etc.) as basic units till the pre-final layer. In the final layer one needs to use the label consistent dictionary learning formulation for classification. We compare our proposed framework with existing state-of-the art deep learning techniques on benchmark problems, we are always within the top 10 results. In actual problems of age and gender classification, we are better than the best known techniques.

This work addresses the problem of estimating proton density and T1 maps from two partially sampled K-space scans such that the total acquisition time remains approximately the same as a single scan. Existing multi-parametric non-linear curve fitting techniques require a large number (8 or more) of echoes to estimate the maps - resulting in prolonged (clinically infeasible) acquisition times. Our simulation results show that our method yields very accurate and robust results from only two partially sampled scans (total scan time being the same as a single echo MRI). We model PD and T1 maps to be sparse in some transform domain. The PD map is recovered via standard Compressed Sensing based recovery technique. Estimating the T1 map requires solving an analysis prior sparse recovery problem from non-linear measurements, since the relationship between T1 values and intensity values / K-space samples is not linear. For the first time in this work, we propose an algorithm for analysis prior sparse recovery for non-linear measurements. We have compared our approach with the only existing technique based on matrix factorization from non-linear measurements, our method yields considerably superior results.

In this work we address the problem of recovering sparse solutions to non-linear inverse problems. We look at two variants of the basic problem - the synthesis prior problem when the solution is sparse and the analysis prior problem where the solution is co-sparse in some linear basis. For the first problem, we propose non-linear variants of the Orthogonal Matching Pursuit (OMP) and CoSamp algorithms, for the second problem we propose a non-linear variant of the Greedy Analysis Pursuit (GAP) algorithm. We empirically test the success rates of our algorithms on exponential and logarithmic functions.

The main challenge faced by wireless sensor networks today is the problem of power consumption at the sensor nodes. Over time, researchers have developed different strategies to address this issue. Such strategies are strongly model dependent and/or application specific. In this work, we take a fresh look at the problem of power consumption in wireless sensor networks from a signal processing perspective. The main idea is simple. Sample only a subset of all the sensor nodes at a given instant and transmit them (this reduces both sampling and communication cost for all the nodes combined). At the central unit (sink) use smart mathematical tools (matrix completion algorithms) to estimate the data for the entire network. We have showed that, if about 1% reconstruction error is allowed, only 20% of the sensors need to sample and transmit at a given instant. This means on an average the life of the network is increased 5-fold. If more error reconstruction error is allowed, even lesser number of sensors need to be active at a given instant leading to more prolonged life of the network.

This paper deals with automatic recognition of offline handwritten Bangla characters. Bangla is the second most popular script among SAARC countries. A new class of features based on Curvelet transform has been used in our classification scheme. The classifier used was SVM with one-against-rest class model. The training and test set were morphologically deformed to get five versions of the same character and each version has been subject to individual SVM classifier. Five classifier outputs obtained in this way have been combined by simple majority voting scheme. The overall recognition accuracy of 95.5% has been obtained on the data set. It is hoped that the Curvelet transform along with such multi-classifier scheme will be useful in other handwritten character data as well.