In this paper, we propose a hierarchical discriminative approach for human action recognition. It consists of feature extraction with mutual motion pattern analysis and discriminative action modeling in the hierarchical manifold space. Hierarchical Gaussian Process Latent Variable Model (HGPLVM) is employed to learn the hierarchical manifold space in which motion patterns are extracted. A casca...

In this paper, we discuss how to approximate the conditional expectation of a random variable Y given a random variable X, i.e. E(Y|X). We propose and compare two different non parametric methodologies to approximate E(Y|X). The first approach (namely the OLP method) is based on a suitable approximation of the σ-algebra generated by X. A second procedure is based on the well known kernel non-pa...

Let (Fn) be an increasing sequence of σ-algebras on some probability space (Ω,F , P ). We will assume that F0 = {∅,Ω}. A sequence (fn) of random variables is called (Fn)adapted if fn is Fn-measurable for each n ≥ 1. In the sequel we will simply write ‘adapted’ if there is no risk of confusion. For any sequence (fn) of random variables, we will write f = supn |fn| and f ∗ n = max1≤k≤n |fk|. Thro...

We present a discriminative, latent variable approach to syntactic parsing in which rules exist at multiple scales of refinement. The model is formally a latent variable CRF grammar over trees, learned by iteratively splitting grammar productions (not categories). Different regions of the grammar are refined to different degrees, yielding grammars which are three orders of magnitude smaller tha...

Conditional Random Fields (CRFs) have shown great success for problems involving structured output variables. However, for many real-world NLP applications, exact maximum-likelihood training is intractable because computing the global normalization factor even approximately can be extremely hard. In addition, optimizing likelihood often does not correlate with maximizing task-specific evaluatio...

In this paper we investigate the use of latent variable structured prediction models for fine-grained sentiment analysis in the common situation where only coarse-grained supervision is available. Specifically, we show how sentencelevel sentiment labels can be effectively learned from document-level supervision using hidden conditional random fields (HCRFs) [10]. Experiments show that this tech...

We present a discriminative part-based approach for the recognition of object classes from unsegmented cluttered scenes. Objects are modeled as flexible constellations of parts conditioned on local observations found by an interest operator. For each object class the probability of a given assignment of parts to local features is modeled by a Conditional Random Field (CRF). We propose an extens...

Generic object recognition by a computer is strongly required in various fields like robot vision and image retrieval in recent years. Conventional methods use Conditional Random Field (CRF) that recognizes the class of each region using the features extracted from the local regions and the class co-occurrence between the adjoining regions. However, there is a problem that CRF tends to fall int...

1 Probability “review” 3 1.1 σ-fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Random variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Probability measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Distribution of a random variable . . . . . . . ....