Abstract: Clustering is an effective technique in data mining to group a set of objects in terms of some attributes. However, most of existing K-Means based clustering algorithms cannot deal with outliers well and are difficult to efficiently solve the problem embedded the L0-norm constraint. To address the above issues and improve the performance of clustering significantly, we propose a novel clustering algorithm, named REFCMFS, which develops a L2,1-norm robust loss as the data-driven item and imposes a L0-norm constraint on the membership matrix to make the model more robust and sparse flexibly. In particular, REFCMFS designs a new way to simplify and solve the L0-norm constraint without any approximate transformation by absorbing 0 into the objective function through a ranking function. These improvements not only make REFCMFS efficiently obtain more promising performance but also provide a new tractable and skillful optimization method to solve the problem embedded the L0-norm constraint.

The course starts with linear models and a discussion of stochastic optimization methods that are crucial for training deep neural networks. Here you can study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course, you can implement a deep neural network for the task of image captioning.

One of the definitional debates that bedevils the artificial intelligence (AI) field is whether machine learning is an AI-based method or technology. Or is it just an analytics-based activity? After all, it is statistical in nature, and attempts--as virtually analytical methods do--to fit a line or curve to a set of data points. And what difference does it make? Basic machine learning is practically indistinguishable from predictive analytics.