Mridula Verma

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Research in the area of privacy preserving techniques in databases and subsequently in data mining concepts have witnessed an explosive growth-spurt in recent years. This work investigates the problem of privacy-preserving mining of frequent sequential patterns over progressive databases. We propose a procedure to protect the privacy of data by adding noisy(More)
Research on pattern mining has deduced that progressive sequential pattern mining approach can be used to obtain the most updated frequent sequential patterns.However, no existing sequential pattern mining algorithms provide a metric to quantify the importance of the extracted sequential patterns. The support count, which can be used as metric, may be(More)
Extensive computational power and the substantial research in the field of image processing and feature extraction automatically generate a need of knowledge discovery from multiple modalities of images. Large number of Flickr images are available and various knowledge discovery research techniques have been applied on this dataset for creating intelligent(More)
One difficulty with machine learning for information extraction is the high cost of collecting labeled examples. Active Learning can make more efficient use of the learner's time by asking them to label only instances that are most useful for the trainer. In random sampling approach, unlabeled data is selected for annotation at random and thus can't yield(More)
Matrix factorization technique has been widely used as a popular method to learn a joint latent-compact subspace, when multiple views or modals of objects (belonging to single-domain or multiple-domain) are available. Our work confronts the problem of learning an informative latent subspace by imparting supervision to matrix factorization for fusing(More)
Ensembling classifiers has been an effective technique for improving performance and generalizability of classification tasks. In a recent research direction, the ensemble of the random projections is being utilized as an effective regularization technique with linear discriminant classifiers. However the framework has only been designed for binary(More)
First order methods are known to be effective for high-dimensional machine learning problems due to their faster convergence and low per-iteration-complexity. In machine learning, many problems are designed as a convex minimization problem with smooth loss function and non-smooth regularizers. Learning with sparsity-inducing regularizers belongs to this(More)