This paper proposes an unsupervised learning framework in which models of objects’ appearance classes are learned using their spatio and temporal information, from video. These models are used to detect objects of different classes in the everyday scene. The proposed technique combines appearance and motion features in a weighted combination framework resulting in models of object classes. Thus...

Recent advancements in unsupervised feature learning have developed powerful latent representations of words. However, it is still not clear what makes one representation better than another and how we can learn the ideal representation. Understanding the structure of latent spaces attained is key to any future advancement in unsupervised learning. In this work, we introduce a new view of conti...

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 UNSUPERVISED REPRESENTATION LEARNING . . . . . . . . . . . . . . . . 4 1.

fusion methods for unsupervised learning ensembles. Book lovers, when you need a new book to read, find the book here. Never worry not to find what you need. Is the fusion methods for unsupervised learning ensembles your needed book now? That's true; you are really a good reader. This is a perfect book that comes from great author to share with you. The book offers the best experience and lesso...

This report gives a brief introduction of our participation in CL-SciSumm 2017 Task 1A. We demonstrate some data analysis and point out the difficulty of this task. Then we report both unsupervised and supervised methods with their performances on 2016 and 2017 testset, from which efficiency of different features can be estimated.

Unsupervised neural learning is typically employed in dimensionality reduction, to extract relevant features for subsequent stages of supervised learning. In this paper we examine a class of unsupervised learning algorithms used for a somewhat different purpose, that of clustering input vectors into various learned stereotyped behaviours in mobile robots [1] . Unsupervised techniques have signi...

In this article, we consider unsupervised learning from the point of view of applying neural computation on signal and data analysis problems. The article is an introductory survey, concentrating on the main principles and categories of unsupervised learning. In neural computation, there are two classical categories for unsupervised learning methods and models: ,rst, extensions of principal com...

As societal concerns on data privacy recently increase, we have witnessed silos among multiple parties in various applications. Federated learning emerges as a new paradigm that enables to collaboratively train machine model without sharing their raw data. Vertical federated learning, where each party owns different features of the same set samples and only single has label, is an important cha...

2 Rate-Distortion Basics 2 2.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Gaussian Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2.1 Sphere-Packing Intuition . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3 Proving Information Rate Distortion = Rate Distortion . . . . . . . . . . . . 4 2.3.1 Convexity of R(...

This report documents the program and the outcomes of Dagstuhl Seminar 16382 “Foundations of Unsupervised Learning”. Unsupervised learning techniques are frequently used in practice of data analysis. However, there is currently little formal guidance as to how, when and to what effect to use which unsupervised learning method. The goal of the seminar was to initiate a broader and more systemati...