In this paper, we evaluate the effect of learning algorithms, unsupervised and supervised, for 3D face model retrieval using a global shape feature. We used the dataset and protocol of SHREC 2007 3D Face Models Track (SHREC 2007 3DFMT) for the evaluation. Unlike the entrants for the track, we used global shape features to capture overall geometric shape of faces, e.g., that of foreheads. One of...

We present a methodology framework for syntactic disambiguation in natural language texts. The method takes advantage of an existing manually compiled non-probabilistic and nonlexicalized grammar, and turns it into a probabilistic lexicalized grammar by automatically learning a kind of subcategorization frames or selectional preferences for all words observed in the training corpus. The diction...

We introduce a new paradigm to investigate unsupervised learning, reducing unsupervised learning to supervised learning. Specifically, we mitigate the subjectivity in unsupervised decision-making by leveraging knowledge acquired from prior, possibly heterogeneous, supervised learning tasks. We demonstrate the versatility of our framework via comprehensive expositions and detailed experiments on...

This paper proposes a novel method for unsupervised ensembles that specifically addresses unbalanced, unsupervised, binary classification problems. Unsupervised learning often experiences the curse of dimensionality, however subspace modeling can overcome this problem. For each subspace created, the classifier produces a decision value. The aggregation of the decision values occurs through the ...

Intelligent creatures can explore their environments and learn useful skills without supervision. In this paper, we propose DIAYN (“Diversity is All You Need”), a method for learning useful skills without a reward function. Our proposed method learns skills by maximizing an information theoretic objective using a maximum entropy policy. On a variety of simulated robotic tasks, we show that this...

In this paper, supervised learning for Self-Generating Neural Networks (SGNN) method, which was originally developed for the purpose of unsupervised learning, is discussed. An information analytical method is proposed to assign weights to attributes in the training examples if class information is available. This significantly improves the learning speed and the accuracy of the SGNN classiier. ...

We consider learning a sequence classifier without labeled data by using sequential output statistics. The problem is highly valuable since obtaining labels in training data is often costly, while the sequential output statistics (e.g., language models) could be obtained independently of input data and thus with low or no cost. To address the problem, we propose an unsupervised learning cost fu...