Trends and Challenges in Lifelong Machine Learning Topic Models

Lifelong learning is the newly introduced approach to machine learning. It follows the true essence of developing learning based intelligent agents. The idea is to build models that can learn with experience in order to become more knowledgeable, following a continuous learning mechanism. It has similarities with other approaches including online learning, transfer learning, reinforcement learning and cumulative learning but has maintained its differences. Lifelong topic modeling refers to the use of lifelong learning approach with topic modeling. The objective is to develop topic models that incorporate past knowledge to generate topics that easily represent the underlying concepts discussed in the dataset. The past knowledge is in the form of a collective sense that helps to give meaningful topics even to domains that have limited or noisy sample data. This review paper puts emphasis on the effectiveness of lifelong learning approach in comparison to the traditional machine learning approaches. It discusses the strengths of lifelong learning and the progress that it has made with topic models as a specific case. In addition the challenges faced by lifelong learning topic models are discussed and the new avenues are highlighted as decisive factors towards the future of lifelong learning approach.