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多模态情绪识别调研问题定义情绪 Emotion多模态 Multi-modal融合算法决策融合特征融合多模态HMM受限/深度玻尔兹曼机（Restricted/Deep Boltzmann Machine）不同设计的网络结构Dense Multimodal FusionMultimodal Transfer ModuleGATED MULTIMODAL UNITSEmotiConLate Fusion of Multimodal InformationCross-Modal Self-Attention Net

Chapter11: Interior-point methods11.1 Inequality constrained minimization problemsIn this chapter we discuss interior-point methods for solving convex optimization problems that include inequality constraints,minimize    f0(x)subject

Chapter10: Equality constrained minimization10.1 Equality constrained minimization problemsConsider a optimization problem:minimize    f(x)subject to    Ax=b\begin{aligned}{\rm minimize} \ \ \ \ & f(x)

Chapter9: Unconstrained minimization9.1 Unconstrained minimization problemsThe unconstrained optimization problem isminimize    f(x)\begin{aligned}{\rm minimize} \ \ \ \ & f(x) \\\end{aligned}minimize    ​

Chapter8: Geometric problems8.1 Projection on a setThe distance of a point x0∈Rnx_0 ∈ \mathbf{R}^nx0​∈Rn to a closed set C⊆RnC⊆\mathbf{R}^nC⊆Rn, in the norm ∥⋅∥∥·∥∥⋅∥, is defined asdist(x0,C)=inf⁡{∣∣x0−x∣∣∣x∈C}{\rm \bold{dist}}(x_0,C)=\inf\{ ||x_0-x||

Chapter7: Statistical estimation7.1 Parametric distribution estimation7.1.1 Maximum likelihood estimationDefine log-likelihood function, and denoted l:l(x)=log⁡px(y)l(x)=\log p_x(y)l(x)=logpx​(y)A widely used method, called maximum likelihood (ML) es

Chapter 6: Approximation and fitting6.1 Norm approximation6.1.1 Basic norm approximation problemThe simplest norm approximation problem is an unconstrained problem of the formminimize    ∣∣Ax−b∣∣{\rm minimize} \ \ \ \ ||Ax-b||min

Chapter5: Duality5.1 The Lagrange dual function5.1.1 The LagrangianConsider an optimization problem in the standard formminimize    f0(x)subject to    fi(x)≤0,i=1,...mhi(x)=0,i=1,...p\begin{aligned}{\rm mi

4 Convex optimization problems4.1 Optimization problems4.1.1 Basic terminologyWe use the notationminimize    f0(x)subject to    fi(x)≤0,i=1,...mhi(x)=0,i=1,...p\begin{aligned}{\rm minimize} \ \ \ \ & f

Chapter3: Convex Functions3.1 Basic properties and examples3.1.1 DefinitionA function f:Rn→Rf:\mathbf{R}^n\rightarrow\mathbf{R}f:Rn→R is a convex function if dom f\mathbf{dom}\space fdom f is convex set and for x,y∈dom f,θ∈[0,1]x,y\in \ma

Chapter2 Convex Set2.1 Affine, Convex Set2.1.1 Line, Line SegmentSuppose x1,x2x_1,x_2x1​,x2​ are two points in Rn\mathbf{R}^nRn,y=θx1+(1−θ)x2,θ∈Ry=\theta x_1+(1-\theta)x_2,\theta\in\mathbf{R}y=θx1​+(1−θ)x2​,θ∈Ris a line. If θ∈[0,1]\theta\in[0,1]θ∈[0,1