Author name cluster

Jan Puzicha

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

NeurIPS Conference 2000 Conference Paper

Shape Context: A New Descriptor for Shape Matching and Object Recognition

Serge Belongie
Jitendra Malik
Jan Puzicha

We develop an approach to object recognition based on match(cid: 173) ing shapes and using a resulting measure of similarity in a nearest neighbor classifier. The key algorithmic problem here is that of finding pointwise correspondences between an image shape and a stored prototype shape. We introduce a new shape descriptor, the shape context, which makes this possible, using a simple and robust algorithm. The shape context at a point captures the distri(cid: 173) bution over relative positions of other shape points and thus sum(cid: 173) marizes global shape in a rich, local descriptor. We demonstrate that shape contexts greatly simplify recovery of correspondences between points of two given shapes. Once shapes are aligned, shape contexts are used to define a robust score for measuring shape sim(cid: 173) ilarity. We have used this score in a nearest-neighbor classifier for recognition of hand written digits as well as 3D objects, using exactly the same distance function. On the benchmark MNIST dataset of handwritten digits, this yields an error rate of 0. 63%, outperforming other published techniques.

PDF Details

IJCAI Conference 1999 Conference Paper

Latent Class Models for Collaborative Filtering

Thomas Hofinann
Jan Puzicha

This paper presents a statistical approach to collaborative filtering and investigates the use of latent class models for predicting individual choices and preferences based on observed preference behavior. Two models are discussed and compared: the aspect model, a probabilistic latent space model which models individual preferences as a convex combination of preference factors, and the two-sided clustering model, which simultaneously partitions persons and objects into clusters. We present EM algorithms for different variants of the aspect model and derive an approximate EM algorithm based on a variational principle for the two-sided clustering model. The benefits of the different models are experimentally investigated on a large movie data set.

PDF

NeurIPS Conference 1998 Conference Paper

Learning from Dyadic Data

Thomas Hofmann
Jan Puzicha
Michael Jordan

Dyadzc data refers to a domain with two finite sets of objects in which observations are made for dyads, i. e. , pairs with one element from either set. This type of data arises naturally in many ap(cid: 173) plication ranging from computational linguistics and information retrieval to preference analysis and computer vision. In this paper, we present a systematic, domain-independent framework of learn(cid: 173) ing from dyadic data by statistical mixture models. Our approach covers different models with fiat and hierarchical latent class struc(cid: 173) tures. We propose an annealed version of the standard EM algo(cid: 173) rithm for model fitting which is empirically evaluated on a variety of data sets from different domains.

PDF Details

NeurIPS Conference 1998 Conference Paper

Visualizing Group Structure

Marcus Held
Jan Puzicha
Joachim Buhmann

Cluster analysis is a fundamental principle in exploratory data analysis, providing the user with a description of the group struc(cid: 173) ture of given data. A key problem in this context is the interpreta(cid: 173) tion and visualization of clustering solutions in high- dimensional or abstract data spaces. In particular, probabilistic descriptions of the group structure, essential to capture inter-cluster relation(cid: 173) ships, are hardly assessable by simple inspection ofthe probabilistic assignment variables. VVe present a novel approach to the visual(cid: 173) ization of group structure. It is based on a statistical model of the object assignments which have been observed or estimated by a probabilistic clustering procedure. The objects or data points are embedded in a low dimensional Euclidean space by approximating the observed data statistics with a Gaussian mixture model. The algorithm provides a new approach to the visualization of the inher(cid: 173) ent structure for a broad variety of data types, e. g. histogram data, proximity data and co-occurrence data. To demonstrate the power of the approach, histograms of textured images are visualized as an example of a large-scale data mining application.

PDF Details