Logistic Regression

• why named logistic regression? (cause losigtic function)
• what is the model?
• how to solve the minimal/maximal problem?

2-class problem

P(y=1|x,θ)=f(x)=11+e−θTx

this logistic (sigmoid) makes probability lies in 0~1

P(y=0|x,θ)=1−f(x)

// also lies in 0~1

in all,

P(y|x,θ)=f(x)y(1−f(x))1−y

假定我們已經學到了最優的θ ，那麼分類的實現是計算P(1|x,θ), if >0.5, then p1−p>1 ; if <0.5, then p1−p<1

學習的目標是最大化整個樣本集合成立的概率：

L(θ)=∏i=1nP(yi|xi,θ)

ℓ(θ)=logL(θ)

then gradient descent could be applied to solve the MLE problem.

Multi-class problem

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0
give the constrain the probability of different class:

∑i=1mP(y(i)=1|x,w)=1

m equals to the class number and w∈Rd×m , where d is the dimension of feature vector x .

P(y(i)=1|x,w)=exp(w(i)Tx)∑mj=1exp(w(j)Tx)for i∈1,...,m

The cost function is

SMLR - Sparse multinomial Logistic Regression

In total m classes, input vector/feature is d -dimensional,
the weight vector for one of the classes need not be estimated. Without loss of generality, we thus set w(m)=0 and the only parameters to be learned are the weight vectors w(i) for i∈1,…,m−1 . For the remainder of the paper, we use w to denote the (d(m-1))-dimensional vector of parameters to be learned.

for ordinary softmax regression (also named as multinomial logistic regression-MLR), the probability that x belongs to class i is written as:

P(y(i)=1|x,w)=exp(w(i)Tx)∑mj=1exp(w(j)Tx)for i∈1,...,m

ℓ(w)=log∏j=1nP(yj|xj,w)

, where n is the total number of samples.

ℓ(w)=∑j=1nlogP(yj|xj,w)

ℓ(w)=∑j=1n∑i=1m1{y(j)=i}logexp(w(i)Txj)∑mj=1exp(w(j)Txj),

where n is the number of samples, m is the number of classes.

ℓ(w)=∑j=1n{∑i=1my(i)jw(i)Txj−log∑i=1mexp(w(i)Txj)}

Besides on, add sparsity constraints to the cost function,

w^MAP=argmaxw{ℓ(w)+logp(w)}

In SMLR, p(w)∝exp(−λ||w||1)